604026304
03
01
01
JB
John Benjamins Publishing Company
01
JB code
IVITRA 24 Eb
15
9789027261397
06
10.1075/ivitra.24
13
2019057309
DG
002
02
01
IVITRA
02
2211-5412
IVITRA Research in Linguistics and Literature
24
01
Computational Phraseology
01
ivitra.24
01
https://benjamins.com
02
https://benjamins.com/catalog/ivitra.24
1
B01
Gloria Corpas Pastor
Corpas Pastor, Gloria
Gloria
Corpas Pastor
University of Malaga
2
B01
Jean-Pierre Colson
Colson, Jean-Pierre
Jean-Pierre
Colson
University of Louvain
01
eng
339
xi
327
LAN009060
v.2006
CFK
2
24
JB Subject Scheme
LIN.COMPUT
Computational & corpus linguistics
24
JB Subject Scheme
LIN.SYNTAX
Syntax
24
JB Subject Scheme
LIN.THEOR
Theoretical linguistics
06
01
Whether you wish to <i>deliver on a promise, take a walk down memory lane</i> or even <i>on the wild side</i>, phraseological units (also often referred to as phrasemes or multiword expressions) are present in most communicative situations and in all world’s languages. <i>Phraseology</i>, the study of phraseological units, has therefore become a rare unifying theme across linguistic theories.<br />In recent years, an increasing number of studies have been concerned with the computational treatment of multiword expressions: these pertain among others to their automatic identification, extraction or translation, and to the role they play in various Natural Language Processing applications. Computational Phraseology is a comparatively new field where better understanding and more advances are urgently needed. This book aims to address this pressing need, by bringing together contributions focusing on different perspectives of this promising interdisciplinary field.
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ivitra.24.forvil
vii
xii
6
Chapter
1
01
Foreword
1
A01
Aline Villavicencio
Villavicencio, Aline
Aline
Villavicencio
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JB code
ivitra.24.00pas
1
8
8
Chapter
2
01
Introduction
1
A01
Gloria Corpas Pastor
Corpas Pastor, Gloria
Gloria
Corpas Pastor
Universidad de Málaga
2
A01
Jean-Pierre Colson
Colson, Jean-Pierre
Jean-Pierre
Colson
Université Catholique de Louvain
10
01
JB code
ivitra.24.01cer
9
22
14
Chapter
3
01
Monocollocable words
A type of language combinatory periphery
1
A01
František Čermák
Čermák, František
František
Čermák
Charles University
20
collocation
20
combination
20
corpus
20
distribution
20
monocollocable
20
periphery
01
How often do people, even native speakers, wonder, on hearing a familiar proverb, such as Much Ado about Nothing, what ado in this proverb really means? Most will know the proverb but their knowledge of ado is often restricted to a particular lexical neighbourhood without realising that it is in fact strongly and prohibitively limited to it in this way. It is not common to give much thought to words in combinations and modes of their combination and realise that some, such as auspices, aback, standstill, ado, may not depend on how the speaker would like to use them and what they choose to say but on what the language dictates to users, that is the way how they must be used. This does not mean that there is much liberty in the use of other words either but these limitations are not immediately obvious as in this case: here, words are in their usage severely restricted to one or few more combinations only. These monocollocable words (as they are termed here), to be found, probably, in all languages, are an obstacle in understanding a foreign language, while, on the other hand, textbooks and dictionaries never really give the user much warning that there is a difficulty related to them if these should be used correctly.
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23
42
20
Chapter
4
01
Translation asymmetries of multiword expressions in machine translation
An analysis of the TED-MWE corpus
1
A01
Johanna Monti
Monti, Johanna
Johanna
Monti
Università degli Studi di Napoli "L'Orientale"
2
A01
Mihael Arcan
Arcan, Mihael
Mihael
Arcan
Insight Centre for Data Analytics
3
A01
Federico Sangati
Sangati, Federico
Federico
Sangati
Università degli Studi di Napoli "L'Orientale"
20
machine translation
20
multiword expressions
20
TED-MWE corpus
20
translation asymmetries
01
Machine Translation (MT) is now extensively used both as a tool to overcome language barriers on the internet and as a professional tool to translate technical documentation. The technology has rapidly evolved in recent years thanks to the availability of large amounts of data in digital format and in particular parallel corpora, which are used to train Statistical Machine Translation (SMT) tools. The quality of MT has considerably improved but the translation of multiword expressions (MWEs) still represents a big and open challenge, both from a theoretical and a practical point of view (Monti, 2013). We define MWEs as any group of two or more words or terms in a language lexicon that generally conveys a single meaning, such as the Italian expressions <i>anima gemella</i> (soul mate), <i>carta di credito</i> (credit card), <i>acqua e sapone</i> (water and soap), <i>piovere a catinelle</i> (rain cats and dogs). The persistence of mistranslation of MWEs in MT outputs originates from their lexical, syntactic, semantic, pragmatic but also translational idiomaticity. Therefore, there is a need to invest in further research in order to achieve significant improvements MT and translation technologies. In particular, it is important to develop resources, mainly MWE-annotated corpora, which can be used for both MT training and evaluation purposes (Monti and Todirascu, 2016). <br />This work focuses on the translation asymmetries between English and Italian MWEs, and how they affect the SMT performance. By translation asymmetries we mean the differences which may occur between an MWE in a source language and its equivalent in the target language, like in many-to-many word translations (En. <i>to be in a position to</i> → It. <i>essere in grado di</i>), many-to-one (En. <i>to set free</i> → It. <i>liberare</i>) and finally one-to-many correspondences (En. <i>overcooked</i> → It. <i>cotto troppo</i>). This chapter describes the evaluation of mistranslations caused by translation asymmetries concerning multiword expressions detected in the TED-MWE corpus (<uri href="http://tiny.cc/TED_MWE">http://tiny.cc/TED_MWE</uri>), which contains 1,500 sentences and 31,000 EN tokens. This corpus is a subset of the TED spoken corpus (Monti et al., 2015) annotated with all the MWEs detected during the evaluation process. The corpus contains the following information: (i) the English source text, (ii) the Italian human translations (from the parallel corpus), and (iii) the Italian SMT output. All the annotators were Italian native speakers with a good knowledge of the English language and with a background in linguistics and computational linguistics. They were asked to identify all MWEs in the source text together with their translations in approximately 300 random sentences each and to evaluate the automatic translation correctness. The identified MWEs and the evaluation of both the human and the machine translation are also recorded in the corpus. This chapter will discuss (i) the related work concerning the impact of anisomorphism (the absence of an exact correspondence between words in two different languages) and the consequent translation asymmetries on MWEs translation quality in MT, (ii) the corpus, (iii) the annotation guidelines, (iv) the methodology adopted during the annotation process (Monti et al., 2015), (v) the results of the annotation and finally (vi) the evaluation of translation asymmetries in the corpus and ideas for future work.
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Chapter
5
01
German constructional phrasemes and their Russian counterparts
A corpus-based study
1
A01
Dmitrij Dobrovol’skij
Dobrovol’skij, Dmitrij
Dmitrij
Dobrovol’skij
Russian Language Institute and Institute of Linguistics, Russian Academy of Sciences/Stockholm University
20
construction grammar
20
constructional phraseme
20
corpora
20
deictic elements
20
German
20
lexicography
20
phraseology
20
Russian
01
In this article I examine a group of semi-fixed German expressions that are irregular with regard to the relationship between form and meaning, namely constructional phrasemes with the deictic elements <i>her</i> ‘hither’ and <i>hin</i> ‘thither’ [<i>vor sich her</i> + V] and [<i>vor sich hin</i> + V]. These constructions pose considerable difficulties not only for the description of their semantics, but also for translation into other languages. Languages such as Russian, English and French do not have exact equivalents of the German deictic elements <i>hin</i> and <i>her</i>. In cases where the German deictic elements <i>her</i> and <i>hin</i> are constituents of relatively fixed and irregular constructions, their meaning fits even less well their standard definition. Using corpus examples, I propose a means of describing these constructional phrasemes in a German-Russian dictionary.
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65
82
18
Chapter
6
01
Computational phraseology and translation studies
From theoretical hypotheses to practical tools
1
A01
Jean-Pierre Colson
Colson, Jean-Pierre
Jean-Pierre
Colson
Université catholique de Louvain
20
computational linguistics
20
interpreting
20
phraseology
20
text mining
20
translation
01
The notion of phraseology is now used across a wide range of linguistic disciplines but it is conspicuously absent from most studies in the area of Translation Studies (e.g. Delisle, 2003; Baker and Saldanha, 2011). The paradox is that many practical difficulties encountered by translators and interpreters are directly related to phraseology in the broad sense (Colson, 2008, 2013), and this can also clearly be seen in the failure of machine translation systems to deal efficiently with the translation of phraseological units (PUs). <br />We argue that phraseology and translation studies have much to gain from cross fertilisation, because both disciplines are regularly criticised for their lack of coherent terminological description and for the insufficient number of reproducible experiments they involve. <br />Decoding phraseology in the source text is far from easy for translators and interpreters, all the more so as they are usually not native speakers of the source language. Finding a natural formulation in the target language and avoiding <i>translationese</i> requires an excellent mastery of the phraseology of the target language. Even experienced professionals sometimes fail to detect the fixed or semi-fixed character of a source text construction. We argue that algorithms derived from text mining and information retrieval techniques can be efficient and (computationally) cost-effective in order to build up unfiltered collections of recurrent fixed or semi-fixed phrases, from which translators could gain information about the number of PUs in the source text. Such an algorithm has been proposed in Colson (2016) and has been implemented in a web application enabling translators and language professionals to automatically retrieve most PUs from a source text. Other tools should be developed in order to bridge the gap between the findings of computational phraseology and the practice of translation and interpreting.
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Chapter
7
01
Computational extraction of formulaic sequences from corpora
Two case studies of a new extraction algorithm
1
A01
Alexander Wahl
Wahl, Alexander
Alexander
Wahl
Donders Institute for Brain, Cognition and Behaviour, Radboud University
2
A01
Stefan Th. Gries
Gries, Stefan Th.
Stefan Th.
Gries
University of California Santa Barbara/Justus Liebig University
20
adjusted frequency list
20
child language
20
collocation extraction
20
formulaic sequences
20
lexical association
20
MERGE
01
We describe a new algorithm for the extraction of formulaic language from corpora. Entitled MERGE (Multi-word Expressions from the Recursive Grouping of Elements), it iteratively combines adjacent bigrams into progressively longer sequences based on lexical association strengths. We then provide empirical evidence for this approach via two case studies. First, we compare the performance of MERGE to that of another algorithm by examining the outputs of the approaches compared with manually annotated formulaic sequences from the spoken component of the British National Corpus. Second, we employ two child language corpora to examine whether MERGE can predict the formulas that the children learn based on caregiver input. Ultimately, we show that MERGE indeed performs well, offering a powerful approach for the extraction of formulas.
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134
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Chapter
8
01
Computational phraseology discovery in corpora with the mwetoolkit
1
A01
Carlos Ramisch
Ramisch, Carlos
Carlos
Ramisch
20
association scores
20
automatic phraseology discovery
20
morphosyntactic patterns
20
mwetoolkit
20
phraseological units
01
Computer tools can help discovering new phraseological units in corpora, thanks to their ability to quickly draw statistics from large amounts of textual data. While the research community has focused on developing and evaluating original algorithms for the automatic discovery of phraseological units, little has been done to transform these sophisticated methods into usable software. In this chapter, we present a brief survey of the main approaches to computational phraseology available. Furthermore, we provide worked out examples of how to apply these methods using the mwetoolkit, a free software for the discovery and identification of multiword ex-pressions. The usefulness of the automatically extracted units depends on various factors such as language, corpus size, target units, and available taggers and parsers. Nonetheless, the mwetoolkit allows fine-grained tuning so that this variability is taken into account, adapting the tool to the specificities of each lexicographic environment.
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150
16
Chapter
9
01
Multiword expressions in comparable corpora
1
A01
Peter Ďurčo
Ďurčo, Peter
Peter
Ďurčo
University of SS. Cyril and Methodius in Trnava
20
comparable corpora
20
compatible Sketch Grammars
20
multiword expressions
20
universal tagset
01
On the basis of Aranea Gigaword Web corpora, a family of comparable corpora intended for use in contrastive linguistic research, multilingual lexicography, language teaching and translation studies we discuss the pros and cons of comparable corpora in contrast to monolingual and parallel corpora for the analysis of multiword entities (MWEs). We demonstrate that by using large corpora for two or more languages, consisting of unrelated texts, yet created in a comparable manner, parallel language structures and phenomena like MWEs can be identified if the appropriate tools are employed. With the Aranea corpora, the “bilingual sketch” functionality of the Sketch Engine is one such tool which provides a new approach for analyses of similarities of (or differences between) collocation profiles (word sketches) for words and their translation equivalents.
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26
Chapter
10
01
Collecting collocations from general and specialised corpora
A comparative analysis
1
A01
Marie-Claude L'Homme
L'Homme, Marie-Claude
Marie-Claude
L'Homme
Observatoire de linguistique Sens-Texte, Université de Montréal
2
A01
Daphnée Azoulay
Azoulay, Daphnée
Daphnée
Azoulay
Observatoire de linguistique Sens-Texte, Université de Montréal
20
classe sémantique
20
Collocation
20
Collocations
20
corpus général
20
corpus spécialisé
20
general corpus
20
lexicographie
20
lexicography
20
semantic class
20
specialised corpus
20
terminologie
20
terminology
01
Collocations are increasingly taken into account in general and specialised repositories and methodologies to collect them are heavily based on corpora. However, lexicographers and terminologists use different kinds of corpora in which combinations are likely to behave according to specific rules and/or patterns. This contribution presents a comparative analysis of the collocational behaviour of 15 lexical items found in a general language corpus and a specialised corpus on the theme of the environment. We automatically extracted large sets of collocates (three lists of 50 collocates) for each lexical item and from each corpus and analyse different facets of collocational behaviour: polysemy of lexical items, characteristics of collocates (overlap, rank and semantic classes of collocates, etc.). Our aim is to draw the attention of terminologists and lexicographers to some specific factors affecting the behaviour of collocations in specialized and general corpora.
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Chapter
11
01
What matters more: The size of the corpora or their quality?
The case of automatic translation of multiword expressions using comparable corpora
1
A01
Ruslan Mitkov
Mitkov, Ruslan
Ruslan
Mitkov
University of Wolverhampton
2
A01
Shiva Taslimipoor
Taslimipoor, Shiva
Shiva
Taslimipoor
University of Wolverhampton
20
automatic translation
20
comparable corpora
20
multiword expressions
20
size of corpora
20
vector representations
01
This study investigates (and compares) the impact of the size and the similarity/quality of comparable corpora on the specific task of extracting translation equivalents of verb-noun collocations from such corpora. The comprehensive evaluation of different configurations of English and Spanish corpora sheds some light on the more general and perennial question: what matters more – the quantity or quality of corpora?
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206
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Chapter
12
01
Statistical significance for measures of collocation strength
1
A01
Michael P. Oakes
Oakes, Michael P.
Michael P.
Oakes
University of Wolverhampton
20
collocation strength
20
Monte Carlo Methods
20
Poisson Distribution
20
statistical significance
01
Of the commonly-used measures of lexical association or collocation strength, only some directly relate to statistical significance: the t-score, chi-squared, log-likelihood, the z-score and Fisher’s exact test. We describe each of these tests, and also describe a computer simulation by which we can derive confidence limits, and hence the statistical significance, of any measure of lexical association which is derived from the contingency table. We illustrate this approach using pointwise mutual information (PMI). We also describe how the Poisson distribution enables us to find the statistical significance of the raw frequency with which a collocation is found. We compare all these methods using collocates of “take”, namely “take up”, “take place”, “take advantage” and “take stock”.
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224
18
Chapter
13
01
Verbal collocations and pronominalisation
1
A01
Eric Wehrli
Wehrli, Eric
Eric
Wehrli
University of Geneva
2
A01
Violeta Seretan
Seretan, Violeta
Violeta
Seretan
University of Geneva
3
A01
Luka Nerima
Nerima, Luka
Luka
Nerima
University of Geneva
20
anaphora resolution
20
collocation
20
deep parsing
20
multiword expressions
20
pronominalisation
01
Precise identification of multiword expressions (MWEs) is an important qualitative step for several NLP applications, including machine translation. Since most MWEs cannot be translated literally, failure to identify them yields, at best, inaccurate translation. While some expressions are completely frozen and thus can be listed as compound words, others display a sometimes very large degree of syntactic flexibility. <br />In this chapter, we argue not only that structural information is necessary for an adequate treatment of collocations, but also that the detection of collocations can be useful for the parser. For instance, it is very useful for solving part-of-speech ambiguities and also some attachment ambiguities. We therefore claim that collocation identification and parsing are interrelated processes. <br />Section 2 describes the two processes of parsing and collocation detection and their interaction, (i) when and how the collocation identification process is triggered during parsing, and (ii) how the identification of a collocation helps the parser. In Section 3 we describe how anaphora resolution has been implemented in our parsing system, to handle cases where the antecedent and the pronoun are within the same sentence or in adjacent sentences. Section 4 focuses on more intricate cases of verbal collocations where their nominal element has been pronominalised, in the form of a relative pronoun or a personal pronoun. Verb-object collocations with a relative pronoun are extremely frequent and relatively easy to handle for a “deep” parser. In most cases, the relative clause is directly attached to the noun which is part of the collocation. Collocations in which the nominal element takes the form of a personal pronoun are much harder to deal with, as they depend on the process of anaphora resolution, a very challenging task. The last section describes an evaluation of the collocation detection procedure, enhanced with anaphora resolution using a corpus of newspaper articles of about 10 million words.
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Chapter
14
01
Empirical variability of Italian multiword expressions as a useful feature for their categorisation
1
A01
Luigi Squillante
Squillante, Luigi
Luigi
Squillante
Sapienza - Università di Roma
20
categorisation
20
collocation
20
multiword expressions
20
PAISÀ corpus
20
semantic variation
01
In contemporary linguistics the definition of those entities which are referred to as multiword expressions (MWEs) remains controversial. It is intuitively clear that some words, when appearing together, have some “special bond” in terms of meaning (e.g. black hole, mountain chain), or lexical choice (e.g. strong tea, to fill a form), contrary to free combinations. Nevertheless, the great variety of features and anomalous behaviours that these expressions exhibit makes it difficult to organise them into categories and gives rise to a great amount of different and sometimes overlapping terminology. <br />So far, most approaches in corpus linguistics have focused on trying to automatically extract MWEs from corpora by using statistical association measures, while theoretical aspects related to their definition, typology and behaviours arising from quantitative corpus-based studies have not been widely explored, especially for languages with a rich morphology and relatively free word order, such as Italian. <br />This contribution attests that a systematic analysis of the empirical behaviour of Italian MWEs in large corpora, with respect to several parameters, such as syntactic and lexical variations, is useful for outlining a categorisation of the expressions in homogeneous sets which approximately correspond to what is intuitively known as multiword units (“polirematiche” in the Italian lexicographic tradition) and lexical collocations. The importance of this kind of approach is that the resulting categorisation of MWEs is grounded on empirical data rather than relying on intuitive and not-always-coherent linguistic definitions. <br />The variational features taken into account are (1) the possibility for the expressions to be syntactically transformed, and (2) the possibility for one of the component to be replaced with a synonym. These features can be automatically and quantitatively investigated using <i>ad hoc</i> designed tools, whose methodology is fully explained, if an annotated corpus and a list of expressions are provided. It is possible to show that the kind of attested variations and the magnitude of variation appear highly correlated to the grammatical structure of a given phrase, indicating that the bond between the components for a multiword unit or a lexical collocation can be formed by activating different kinds of restrictions, depending on the considered grammatical pattern.
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Chapter
15
01
<i>Too big to fail</i> but <i>big enough to pay for their mistakes</i>
A collostructional analysis of the patterns [ <i>too</i> ADJ <i>to</i> V] and [ADJ <i>enough to</i> V]
1
A01
Anatol Stefanowitsch
Stefanowitsch, Anatol
Anatol
Stefanowitsch
Freie Universität Berlin
2
A01
Susanne Flach
Flach, Susanne
Susanne
Flach
Université de Neuchâtel
20
association
20
collocations
20
Collostructional Analysis
20
collostructions
20
Co-Varying Collexeme Analysis
20
Distinctive Collexeme Analysis
20
Distinctive Co-varying Collexeme Analysis
20
Simple Collexeme Analysis
01
In this paper, we illustrate the usefulness of the family of methods collectively known as Collostructional Analysis for phraseological research. Investigating two patterns, [<i>too</i> ADJ <i>to</i> V] and [ADJ <i>enough to</i> V], we show how a technique originally developed for the investigation of words and constructions can be fruitfully applied to issues pertinent to phraseology, such as the co-existence of compositional and idiomatic semantics and the analysis of semantically complementary patterns more generally. To this end, we use the three conventional methods (Simple, Distinctive and Co-varying Collexeme Analyses) and propose a novel extension (Distinctive Co-varying Collexeme Analysis) particularly suitable for the investigation of complementary patterns. We show that collostructional analysis is suitable for confirming hypotheses derived from qualitative analyses, as well as uncovering subtle differences that are otherwise inaccessible for non-empirical research.
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Chapter
16
01
Multi-word patterns and networks
How corpus-driven approaches have changed our description of language use
1
A01
Kathrin Steyer
Steyer, Kathrin
Kathrin
Steyer
Institut für Deutsche Sprache
20
German reference corpus
20
language fixedness
20
multiword expressions
20
pattern-based lexicography
20
phraseology
01
This paper discusses a theoretical and empirical approach to language fixedness that we have developed at the Institut für Deutsche Sprache (IDS) (‘Institute for German Language’) in Mannheim in the project Usuelle Worterbindungen (UWV) over the last decade. The analysis described is based on the Deutsches Referenzkorpus (‘German Reference Corpus’; DeReKo) which is located at the IDS. The corpus analysis tool used for accessing the corpus data is COSMAS II (CII) and – for statistical analysis – the IDS collocation analysis tool (Belica, 1995; CA). For detecting lexical patterns and describing their semantic and pragmatic nature we use the tool lexpan (or ‘Lexical Pattern Analyzer’) that was developed in our project. We discuss a new corpus-driven pattern dictionary that is relevant not only to the field of phraseology, but also to usage-based linguistics and lexicography as a whole.
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310
14
Chapter
17
01
How context determines meaning
1
A01
Patrick Hanks
Hanks, Patrick
Patrick
Hanks
RIILP, University of Wolverhampton (WLV) and BCL, University of the West of England (UWE)
20
collocation
20
corpus pattern analysis (CPA)
20
lexical sets
20
meaning potential
20
valency
01
It is an extraordinary fact that, although most speakers and writers of the English language (or, we may presume, any other language) believe that they are capable of expressing any meaning that they want to with considerable precision, the behaviour of the words they use is highly variable, with much variation in phraseology as well as subtle semantic distinctions. Even more extraordinary is the fact that only some of the logically predictable variants of any given phrase are accepted by native speakers as idiomatic. <br />This chapter shows how meanings are associated with phraseological norms rather than with words in isolation. It also illustrates the phenomenon of alternation among phraseological norms and shows how phraseological norms are not merely conformed to, but also exploited creatively in ordinary language use. Underlying this paper is the proposition that words in isolation do not have a determinable meaning per se. Instead they have <b>meaning potential</b>, different facets of which are activated in different contexts. <br />By detailed corpus pattern analysis of the verb <i>blow</i>, which typically expresses the causation of movement, we explore the relationship between core meaning and a rich set of patterns of idiomatic phraseology – phrasal verbs, idioms, and proverbs.
10
01
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Chapter
18
01
Detecting semantic difference
A new model based on knowledge and collocational association
1
A01
Shiva Taslimipoor
Taslimipoor, Shiva
Shiva
Taslimipoor
Research Group in Computational Linguistics, University of Wolverhampton
2
A01
Gloria Corpas Pastor
Corpas Pastor, Gloria
Gloria
Corpas Pastor
Research Group in Computational Linguistics, University of Wolverhampton/University of Malaga
3
A01
Omid Rohanian
Rohanian, Omid
Omid
Rohanian
Research Group in Computational Linguistics, University of Wolverhampton
20
association measures
20
collocation
20
Concept-Net relations
20
n-gram counts
20
semantic difference
20
semantic modelling
20
word2vec
01
Semantic discrimination among concepts is a daily exercise for humans when using natural languages. For example, given the words, <i>airplane</i> and <i>car</i>, the word <i>flying</i> can easily be thought and used as an attribute to differentiate them. In this study, we propose a novel automatic approach to detect whether an attribute word represents the difference between two given words. We exploit a combination of knowledge-based and co-occurrence features (collocations) to capture the semantic difference between two words in relation to an attribute. The features are scores that are defined for each pair of words and an attribute, based on association measures, n-gram counts, word similarity, and Concept-Net relations. Based on these features we designed a system that run several experiments on a SemEval-2018 dataset. The experimental results indicate that the proposed model performs better, or at least comparable with, other systems evaluated on the same data for this task.
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327
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Miscellaneous
19
01
Index
02
JBENJAMINS
John Benjamins Publishing Company
01
John Benjamins Publishing Company
Amsterdam/Philadelphia
NL
04
20200508
2020
John Benjamins B.V.
02
WORLD
13
15
9789027205353
01
JB
3
John Benjamins e-Platform
03
jbe-platform.com
09
WORLD
21
01
00
99.00
EUR
R
01
00
83.00
GBP
Z
01
gen
00
149.00
USD
S
923026303
03
01
01
JB
John Benjamins Publishing Company
01
JB code
IVITRA 24 Hb
15
9789027205353
13
2019057308
BB
01
IVITRA
02
2211-5412
IVITRA Research in Linguistics and Literature
24
01
Computational Phraseology
01
ivitra.24
01
https://benjamins.com
02
https://benjamins.com/catalog/ivitra.24
1
B01
Gloria Corpas Pastor
Corpas Pastor, Gloria
Gloria
Corpas Pastor
University of Malaga
2
B01
Jean-Pierre Colson
Colson, Jean-Pierre
Jean-Pierre
Colson
University of Louvain
01
eng
339
xi
327
LAN009060
v.2006
CFK
2
24
JB Subject Scheme
LIN.COMPUT
Computational & corpus linguistics
24
JB Subject Scheme
LIN.SYNTAX
Syntax
24
JB Subject Scheme
LIN.THEOR
Theoretical linguistics
06
01
Whether you wish to <i>deliver on a promise, take a walk down memory lane</i> or even <i>on the wild side</i>, phraseological units (also often referred to as phrasemes or multiword expressions) are present in most communicative situations and in all world’s languages. <i>Phraseology</i>, the study of phraseological units, has therefore become a rare unifying theme across linguistic theories.<br />In recent years, an increasing number of studies have been concerned with the computational treatment of multiword expressions: these pertain among others to their automatic identification, extraction or translation, and to the role they play in various Natural Language Processing applications. Computational Phraseology is a comparatively new field where better understanding and more advances are urgently needed. This book aims to address this pressing need, by bringing together contributions focusing on different perspectives of this promising interdisciplinary field.
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ivitra.24.forvil
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xii
6
Chapter
1
01
Foreword
1
A01
Aline Villavicencio
Villavicencio, Aline
Aline
Villavicencio
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ivitra.24.00pas
1
8
8
Chapter
2
01
Introduction
1
A01
Gloria Corpas Pastor
Corpas Pastor, Gloria
Gloria
Corpas Pastor
Universidad de Málaga
2
A01
Jean-Pierre Colson
Colson, Jean-Pierre
Jean-Pierre
Colson
Université Catholique de Louvain
10
01
JB code
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9
22
14
Chapter
3
01
Monocollocable words
A type of language combinatory periphery
1
A01
František Čermák
Čermák, František
František
Čermák
Charles University
20
collocation
20
combination
20
corpus
20
distribution
20
monocollocable
20
periphery
01
How often do people, even native speakers, wonder, on hearing a familiar proverb, such as Much Ado about Nothing, what ado in this proverb really means? Most will know the proverb but their knowledge of ado is often restricted to a particular lexical neighbourhood without realising that it is in fact strongly and prohibitively limited to it in this way. It is not common to give much thought to words in combinations and modes of their combination and realise that some, such as auspices, aback, standstill, ado, may not depend on how the speaker would like to use them and what they choose to say but on what the language dictates to users, that is the way how they must be used. This does not mean that there is much liberty in the use of other words either but these limitations are not immediately obvious as in this case: here, words are in their usage severely restricted to one or few more combinations only. These monocollocable words (as they are termed here), to be found, probably, in all languages, are an obstacle in understanding a foreign language, while, on the other hand, textbooks and dictionaries never really give the user much warning that there is a difficulty related to them if these should be used correctly.
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42
20
Chapter
4
01
Translation asymmetries of multiword expressions in machine translation
An analysis of the TED-MWE corpus
1
A01
Johanna Monti
Monti, Johanna
Johanna
Monti
Università degli Studi di Napoli "L'Orientale"
2
A01
Mihael Arcan
Arcan, Mihael
Mihael
Arcan
Insight Centre for Data Analytics
3
A01
Federico Sangati
Sangati, Federico
Federico
Sangati
Università degli Studi di Napoli "L'Orientale"
20
machine translation
20
multiword expressions
20
TED-MWE corpus
20
translation asymmetries
01
Machine Translation (MT) is now extensively used both as a tool to overcome language barriers on the internet and as a professional tool to translate technical documentation. The technology has rapidly evolved in recent years thanks to the availability of large amounts of data in digital format and in particular parallel corpora, which are used to train Statistical Machine Translation (SMT) tools. The quality of MT has considerably improved but the translation of multiword expressions (MWEs) still represents a big and open challenge, both from a theoretical and a practical point of view (Monti, 2013). We define MWEs as any group of two or more words or terms in a language lexicon that generally conveys a single meaning, such as the Italian expressions <i>anima gemella</i> (soul mate), <i>carta di credito</i> (credit card), <i>acqua e sapone</i> (water and soap), <i>piovere a catinelle</i> (rain cats and dogs). The persistence of mistranslation of MWEs in MT outputs originates from their lexical, syntactic, semantic, pragmatic but also translational idiomaticity. Therefore, there is a need to invest in further research in order to achieve significant improvements MT and translation technologies. In particular, it is important to develop resources, mainly MWE-annotated corpora, which can be used for both MT training and evaluation purposes (Monti and Todirascu, 2016). <br />This work focuses on the translation asymmetries between English and Italian MWEs, and how they affect the SMT performance. By translation asymmetries we mean the differences which may occur between an MWE in a source language and its equivalent in the target language, like in many-to-many word translations (En. <i>to be in a position to</i> → It. <i>essere in grado di</i>), many-to-one (En. <i>to set free</i> → It. <i>liberare</i>) and finally one-to-many correspondences (En. <i>overcooked</i> → It. <i>cotto troppo</i>). This chapter describes the evaluation of mistranslations caused by translation asymmetries concerning multiword expressions detected in the TED-MWE corpus (<uri href="http://tiny.cc/TED_MWE">http://tiny.cc/TED_MWE</uri>), which contains 1,500 sentences and 31,000 EN tokens. This corpus is a subset of the TED spoken corpus (Monti et al., 2015) annotated with all the MWEs detected during the evaluation process. The corpus contains the following information: (i) the English source text, (ii) the Italian human translations (from the parallel corpus), and (iii) the Italian SMT output. All the annotators were Italian native speakers with a good knowledge of the English language and with a background in linguistics and computational linguistics. They were asked to identify all MWEs in the source text together with their translations in approximately 300 random sentences each and to evaluate the automatic translation correctness. The identified MWEs and the evaluation of both the human and the machine translation are also recorded in the corpus. This chapter will discuss (i) the related work concerning the impact of anisomorphism (the absence of an exact correspondence between words in two different languages) and the consequent translation asymmetries on MWEs translation quality in MT, (ii) the corpus, (iii) the annotation guidelines, (iv) the methodology adopted during the annotation process (Monti et al., 2015), (v) the results of the annotation and finally (vi) the evaluation of translation asymmetries in the corpus and ideas for future work.
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Chapter
5
01
German constructional phrasemes and their Russian counterparts
A corpus-based study
1
A01
Dmitrij Dobrovol’skij
Dobrovol’skij, Dmitrij
Dmitrij
Dobrovol’skij
Russian Language Institute and Institute of Linguistics, Russian Academy of Sciences/Stockholm University
20
construction grammar
20
constructional phraseme
20
corpora
20
deictic elements
20
German
20
lexicography
20
phraseology
20
Russian
01
In this article I examine a group of semi-fixed German expressions that are irregular with regard to the relationship between form and meaning, namely constructional phrasemes with the deictic elements <i>her</i> ‘hither’ and <i>hin</i> ‘thither’ [<i>vor sich her</i> + V] and [<i>vor sich hin</i> + V]. These constructions pose considerable difficulties not only for the description of their semantics, but also for translation into other languages. Languages such as Russian, English and French do not have exact equivalents of the German deictic elements <i>hin</i> and <i>her</i>. In cases where the German deictic elements <i>her</i> and <i>hin</i> are constituents of relatively fixed and irregular constructions, their meaning fits even less well their standard definition. Using corpus examples, I propose a means of describing these constructional phrasemes in a German-Russian dictionary.
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Chapter
6
01
Computational phraseology and translation studies
From theoretical hypotheses to practical tools
1
A01
Jean-Pierre Colson
Colson, Jean-Pierre
Jean-Pierre
Colson
Université catholique de Louvain
20
computational linguistics
20
interpreting
20
phraseology
20
text mining
20
translation
01
The notion of phraseology is now used across a wide range of linguistic disciplines but it is conspicuously absent from most studies in the area of Translation Studies (e.g. Delisle, 2003; Baker and Saldanha, 2011). The paradox is that many practical difficulties encountered by translators and interpreters are directly related to phraseology in the broad sense (Colson, 2008, 2013), and this can also clearly be seen in the failure of machine translation systems to deal efficiently with the translation of phraseological units (PUs). <br />We argue that phraseology and translation studies have much to gain from cross fertilisation, because both disciplines are regularly criticised for their lack of coherent terminological description and for the insufficient number of reproducible experiments they involve. <br />Decoding phraseology in the source text is far from easy for translators and interpreters, all the more so as they are usually not native speakers of the source language. Finding a natural formulation in the target language and avoiding <i>translationese</i> requires an excellent mastery of the phraseology of the target language. Even experienced professionals sometimes fail to detect the fixed or semi-fixed character of a source text construction. We argue that algorithms derived from text mining and information retrieval techniques can be efficient and (computationally) cost-effective in order to build up unfiltered collections of recurrent fixed or semi-fixed phrases, from which translators could gain information about the number of PUs in the source text. Such an algorithm has been proposed in Colson (2016) and has been implemented in a web application enabling translators and language professionals to automatically retrieve most PUs from a source text. Other tools should be developed in order to bridge the gap between the findings of computational phraseology and the practice of translation and interpreting.
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Chapter
7
01
Computational extraction of formulaic sequences from corpora
Two case studies of a new extraction algorithm
1
A01
Alexander Wahl
Wahl, Alexander
Alexander
Wahl
Donders Institute for Brain, Cognition and Behaviour, Radboud University
2
A01
Stefan Th. Gries
Gries, Stefan Th.
Stefan Th.
Gries
University of California Santa Barbara/Justus Liebig University
20
adjusted frequency list
20
child language
20
collocation extraction
20
formulaic sequences
20
lexical association
20
MERGE
01
We describe a new algorithm for the extraction of formulaic language from corpora. Entitled MERGE (Multi-word Expressions from the Recursive Grouping of Elements), it iteratively combines adjacent bigrams into progressively longer sequences based on lexical association strengths. We then provide empirical evidence for this approach via two case studies. First, we compare the performance of MERGE to that of another algorithm by examining the outputs of the approaches compared with manually annotated formulaic sequences from the spoken component of the British National Corpus. Second, we employ two child language corpora to examine whether MERGE can predict the formulas that the children learn based on caregiver input. Ultimately, we show that MERGE indeed performs well, offering a powerful approach for the extraction of formulas.
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Chapter
8
01
Computational phraseology discovery in corpora with the mwetoolkit
1
A01
Carlos Ramisch
Ramisch, Carlos
Carlos
Ramisch
20
association scores
20
automatic phraseology discovery
20
morphosyntactic patterns
20
mwetoolkit
20
phraseological units
01
Computer tools can help discovering new phraseological units in corpora, thanks to their ability to quickly draw statistics from large amounts of textual data. While the research community has focused on developing and evaluating original algorithms for the automatic discovery of phraseological units, little has been done to transform these sophisticated methods into usable software. In this chapter, we present a brief survey of the main approaches to computational phraseology available. Furthermore, we provide worked out examples of how to apply these methods using the mwetoolkit, a free software for the discovery and identification of multiword ex-pressions. The usefulness of the automatically extracted units depends on various factors such as language, corpus size, target units, and available taggers and parsers. Nonetheless, the mwetoolkit allows fine-grained tuning so that this variability is taken into account, adapting the tool to the specificities of each lexicographic environment.
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150
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Chapter
9
01
Multiword expressions in comparable corpora
1
A01
Peter Ďurčo
Ďurčo, Peter
Peter
Ďurčo
University of SS. Cyril and Methodius in Trnava
20
comparable corpora
20
compatible Sketch Grammars
20
multiword expressions
20
universal tagset
01
On the basis of Aranea Gigaword Web corpora, a family of comparable corpora intended for use in contrastive linguistic research, multilingual lexicography, language teaching and translation studies we discuss the pros and cons of comparable corpora in contrast to monolingual and parallel corpora for the analysis of multiword entities (MWEs). We demonstrate that by using large corpora for two or more languages, consisting of unrelated texts, yet created in a comparable manner, parallel language structures and phenomena like MWEs can be identified if the appropriate tools are employed. With the Aranea corpora, the “bilingual sketch” functionality of the Sketch Engine is one such tool which provides a new approach for analyses of similarities of (or differences between) collocation profiles (word sketches) for words and their translation equivalents.
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Chapter
10
01
Collecting collocations from general and specialised corpora
A comparative analysis
1
A01
Marie-Claude L'Homme
L'Homme, Marie-Claude
Marie-Claude
L'Homme
Observatoire de linguistique Sens-Texte, Université de Montréal
2
A01
Daphnée Azoulay
Azoulay, Daphnée
Daphnée
Azoulay
Observatoire de linguistique Sens-Texte, Université de Montréal
20
classe sémantique
20
Collocation
20
Collocations
20
corpus général
20
corpus spécialisé
20
general corpus
20
lexicographie
20
lexicography
20
semantic class
20
specialised corpus
20
terminologie
20
terminology
01
Collocations are increasingly taken into account in general and specialised repositories and methodologies to collect them are heavily based on corpora. However, lexicographers and terminologists use different kinds of corpora in which combinations are likely to behave according to specific rules and/or patterns. This contribution presents a comparative analysis of the collocational behaviour of 15 lexical items found in a general language corpus and a specialised corpus on the theme of the environment. We automatically extracted large sets of collocates (three lists of 50 collocates) for each lexical item and from each corpus and analyse different facets of collocational behaviour: polysemy of lexical items, characteristics of collocates (overlap, rank and semantic classes of collocates, etc.). Our aim is to draw the attention of terminologists and lexicographers to some specific factors affecting the behaviour of collocations in specialized and general corpora.
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11
01
What matters more: The size of the corpora or their quality?
The case of automatic translation of multiword expressions using comparable corpora
1
A01
Ruslan Mitkov
Mitkov, Ruslan
Ruslan
Mitkov
University of Wolverhampton
2
A01
Shiva Taslimipoor
Taslimipoor, Shiva
Shiva
Taslimipoor
University of Wolverhampton
20
automatic translation
20
comparable corpora
20
multiword expressions
20
size of corpora
20
vector representations
01
This study investigates (and compares) the impact of the size and the similarity/quality of comparable corpora on the specific task of extracting translation equivalents of verb-noun collocations from such corpora. The comprehensive evaluation of different configurations of English and Spanish corpora sheds some light on the more general and perennial question: what matters more – the quantity or quality of corpora?
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Chapter
12
01
Statistical significance for measures of collocation strength
1
A01
Michael P. Oakes
Oakes, Michael P.
Michael P.
Oakes
University of Wolverhampton
20
collocation strength
20
Monte Carlo Methods
20
Poisson Distribution
20
statistical significance
01
Of the commonly-used measures of lexical association or collocation strength, only some directly relate to statistical significance: the t-score, chi-squared, log-likelihood, the z-score and Fisher’s exact test. We describe each of these tests, and also describe a computer simulation by which we can derive confidence limits, and hence the statistical significance, of any measure of lexical association which is derived from the contingency table. We illustrate this approach using pointwise mutual information (PMI). We also describe how the Poisson distribution enables us to find the statistical significance of the raw frequency with which a collocation is found. We compare all these methods using collocates of “take”, namely “take up”, “take place”, “take advantage” and “take stock”.
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224
18
Chapter
13
01
Verbal collocations and pronominalisation
1
A01
Eric Wehrli
Wehrli, Eric
Eric
Wehrli
University of Geneva
2
A01
Violeta Seretan
Seretan, Violeta
Violeta
Seretan
University of Geneva
3
A01
Luka Nerima
Nerima, Luka
Luka
Nerima
University of Geneva
20
anaphora resolution
20
collocation
20
deep parsing
20
multiword expressions
20
pronominalisation
01
Precise identification of multiword expressions (MWEs) is an important qualitative step for several NLP applications, including machine translation. Since most MWEs cannot be translated literally, failure to identify them yields, at best, inaccurate translation. While some expressions are completely frozen and thus can be listed as compound words, others display a sometimes very large degree of syntactic flexibility. <br />In this chapter, we argue not only that structural information is necessary for an adequate treatment of collocations, but also that the detection of collocations can be useful for the parser. For instance, it is very useful for solving part-of-speech ambiguities and also some attachment ambiguities. We therefore claim that collocation identification and parsing are interrelated processes. <br />Section 2 describes the two processes of parsing and collocation detection and their interaction, (i) when and how the collocation identification process is triggered during parsing, and (ii) how the identification of a collocation helps the parser. In Section 3 we describe how anaphora resolution has been implemented in our parsing system, to handle cases where the antecedent and the pronoun are within the same sentence or in adjacent sentences. Section 4 focuses on more intricate cases of verbal collocations where their nominal element has been pronominalised, in the form of a relative pronoun or a personal pronoun. Verb-object collocations with a relative pronoun are extremely frequent and relatively easy to handle for a “deep” parser. In most cases, the relative clause is directly attached to the noun which is part of the collocation. Collocations in which the nominal element takes the form of a personal pronoun are much harder to deal with, as they depend on the process of anaphora resolution, a very challenging task. The last section describes an evaluation of the collocation detection procedure, enhanced with anaphora resolution using a corpus of newspaper articles of about 10 million words.
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Chapter
14
01
Empirical variability of Italian multiword expressions as a useful feature for their categorisation
1
A01
Luigi Squillante
Squillante, Luigi
Luigi
Squillante
Sapienza - Università di Roma
20
categorisation
20
collocation
20
multiword expressions
20
PAISÀ corpus
20
semantic variation
01
In contemporary linguistics the definition of those entities which are referred to as multiword expressions (MWEs) remains controversial. It is intuitively clear that some words, when appearing together, have some “special bond” in terms of meaning (e.g. black hole, mountain chain), or lexical choice (e.g. strong tea, to fill a form), contrary to free combinations. Nevertheless, the great variety of features and anomalous behaviours that these expressions exhibit makes it difficult to organise them into categories and gives rise to a great amount of different and sometimes overlapping terminology. <br />So far, most approaches in corpus linguistics have focused on trying to automatically extract MWEs from corpora by using statistical association measures, while theoretical aspects related to their definition, typology and behaviours arising from quantitative corpus-based studies have not been widely explored, especially for languages with a rich morphology and relatively free word order, such as Italian. <br />This contribution attests that a systematic analysis of the empirical behaviour of Italian MWEs in large corpora, with respect to several parameters, such as syntactic and lexical variations, is useful for outlining a categorisation of the expressions in homogeneous sets which approximately correspond to what is intuitively known as multiword units (“polirematiche” in the Italian lexicographic tradition) and lexical collocations. The importance of this kind of approach is that the resulting categorisation of MWEs is grounded on empirical data rather than relying on intuitive and not-always-coherent linguistic definitions. <br />The variational features taken into account are (1) the possibility for the expressions to be syntactically transformed, and (2) the possibility for one of the component to be replaced with a synonym. These features can be automatically and quantitatively investigated using <i>ad hoc</i> designed tools, whose methodology is fully explained, if an annotated corpus and a list of expressions are provided. It is possible to show that the kind of attested variations and the magnitude of variation appear highly correlated to the grammatical structure of a given phrase, indicating that the bond between the components for a multiword unit or a lexical collocation can be formed by activating different kinds of restrictions, depending on the considered grammatical pattern.
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15
01
<i>Too big to fail</i> but <i>big enough to pay for their mistakes</i>
A collostructional analysis of the patterns [ <i>too</i> ADJ <i>to</i> V] and [ADJ <i>enough to</i> V]
1
A01
Anatol Stefanowitsch
Stefanowitsch, Anatol
Anatol
Stefanowitsch
Freie Universität Berlin
2
A01
Susanne Flach
Flach, Susanne
Susanne
Flach
Université de Neuchâtel
20
association
20
collocations
20
Collostructional Analysis
20
collostructions
20
Co-Varying Collexeme Analysis
20
Distinctive Collexeme Analysis
20
Distinctive Co-varying Collexeme Analysis
20
Simple Collexeme Analysis
01
In this paper, we illustrate the usefulness of the family of methods collectively known as Collostructional Analysis for phraseological research. Investigating two patterns, [<i>too</i> ADJ <i>to</i> V] and [ADJ <i>enough to</i> V], we show how a technique originally developed for the investigation of words and constructions can be fruitfully applied to issues pertinent to phraseology, such as the co-existence of compositional and idiomatic semantics and the analysis of semantically complementary patterns more generally. To this end, we use the three conventional methods (Simple, Distinctive and Co-varying Collexeme Analyses) and propose a novel extension (Distinctive Co-varying Collexeme Analysis) particularly suitable for the investigation of complementary patterns. We show that collostructional analysis is suitable for confirming hypotheses derived from qualitative analyses, as well as uncovering subtle differences that are otherwise inaccessible for non-empirical research.
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296
24
Chapter
16
01
Multi-word patterns and networks
How corpus-driven approaches have changed our description of language use
1
A01
Kathrin Steyer
Steyer, Kathrin
Kathrin
Steyer
Institut für Deutsche Sprache
20
German reference corpus
20
language fixedness
20
multiword expressions
20
pattern-based lexicography
20
phraseology
01
This paper discusses a theoretical and empirical approach to language fixedness that we have developed at the Institut für Deutsche Sprache (IDS) (‘Institute for German Language’) in Mannheim in the project Usuelle Worterbindungen (UWV) over the last decade. The analysis described is based on the Deutsches Referenzkorpus (‘German Reference Corpus’; DeReKo) which is located at the IDS. The corpus analysis tool used for accessing the corpus data is COSMAS II (CII) and – for statistical analysis – the IDS collocation analysis tool (Belica, 1995; CA). For detecting lexical patterns and describing their semantic and pragmatic nature we use the tool lexpan (or ‘Lexical Pattern Analyzer’) that was developed in our project. We discuss a new corpus-driven pattern dictionary that is relevant not only to the field of phraseology, but also to usage-based linguistics and lexicography as a whole.
10
01
JB code
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310
14
Chapter
17
01
How context determines meaning
1
A01
Patrick Hanks
Hanks, Patrick
Patrick
Hanks
RIILP, University of Wolverhampton (WLV) and BCL, University of the West of England (UWE)
20
collocation
20
corpus pattern analysis (CPA)
20
lexical sets
20
meaning potential
20
valency
01
It is an extraordinary fact that, although most speakers and writers of the English language (or, we may presume, any other language) believe that they are capable of expressing any meaning that they want to with considerable precision, the behaviour of the words they use is highly variable, with much variation in phraseology as well as subtle semantic distinctions. Even more extraordinary is the fact that only some of the logically predictable variants of any given phrase are accepted by native speakers as idiomatic. <br />This chapter shows how meanings are associated with phraseological norms rather than with words in isolation. It also illustrates the phenomenon of alternation among phraseological norms and shows how phraseological norms are not merely conformed to, but also exploited creatively in ordinary language use. Underlying this paper is the proposition that words in isolation do not have a determinable meaning per se. Instead they have <b>meaning potential</b>, different facets of which are activated in different contexts. <br />By detailed corpus pattern analysis of the verb <i>blow</i>, which typically expresses the causation of movement, we explore the relationship between core meaning and a rich set of patterns of idiomatic phraseology – phrasal verbs, idioms, and proverbs.
10
01
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324
14
Chapter
18
01
Detecting semantic difference
A new model based on knowledge and collocational association
1
A01
Shiva Taslimipoor
Taslimipoor, Shiva
Shiva
Taslimipoor
Research Group in Computational Linguistics, University of Wolverhampton
2
A01
Gloria Corpas Pastor
Corpas Pastor, Gloria
Gloria
Corpas Pastor
Research Group in Computational Linguistics, University of Wolverhampton/University of Malaga
3
A01
Omid Rohanian
Rohanian, Omid
Omid
Rohanian
Research Group in Computational Linguistics, University of Wolverhampton
20
association measures
20
collocation
20
Concept-Net relations
20
n-gram counts
20
semantic difference
20
semantic modelling
20
word2vec
01
Semantic discrimination among concepts is a daily exercise for humans when using natural languages. For example, given the words, <i>airplane</i> and <i>car</i>, the word <i>flying</i> can easily be thought and used as an attribute to differentiate them. In this study, we propose a novel automatic approach to detect whether an attribute word represents the difference between two given words. We exploit a combination of knowledge-based and co-occurrence features (collocations) to capture the semantic difference between two words in relation to an attribute. The features are scores that are defined for each pair of words and an attribute, based on association measures, n-gram counts, word similarity, and Concept-Net relations. Based on these features we designed a system that run several experiments on a SemEval-2018 dataset. The experimental results indicate that the proposed model performs better, or at least comparable with, other systems evaluated on the same data for this task.
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JB code
ivitra.24.index
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327
3
Miscellaneous
19
01
Index
02
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