Preliminary findings from corpus analysis suggest that the semantics of each verb in the language are determined by the totality of its complementation patterns. Accurate description of those patterns requires a level of analytic delicacy which was not possible until the advent of large bodies of data, along with techniques for distinguishing significant patterns from mere noise. Such analysis is in its infancy, but it is already clear that, in order to analyse the semantics of verbs empirically, it is necessary to identify typical subjects, objects, and adverbials and to group individual lexical items into sets within those clause roles. The nature of lexical sets is discussed and an attempt is made to indicate the range of semantic and syntactic phenomena likely to be encountered in lexical analysis of this kind.
2019. From visual perception to evidentiality: A functional empirical approach to se ve que in Spanish. Lingua 220 ► pp. 76 ff.
Boholm, Max & Åsa Boholm
2020. Risk Identification: A Corpus‐Assisted Study of Websites of Government Agencies. Risk, Hazards & Crisis in Public Policy 11:3 ► pp. 242 ff.
Boholm, Max, Niklas Möller & Sven Ove Hansson
2016. The Concepts of Risk, Safety, and Security: Applications in Everyday Language. Risk Analysis 36:2 ► pp. 320 ff.
Brown, Susan Windisch, Dmitriy Dligach & Martha Palmer
2014. VerbNet Class Assignment as a WSD Task. In Computing Meaning [Text, Speech and Language Technology, 47], ► pp. 203 ff.
Bębeniec, Daria
2024. In search of methodological standards for corpus-based cognitive semantics: The case of Behavioral Profiles. Studia Neophilologica► pp. 1 ff.
Chen, Alvin Cheng-Hsien
2022. Words, constructions and corpora: Network representations of constructional semantics for Mandarin space particles. Corpus Linguistics and Linguistic Theory 18:2 ► pp. 209 ff.
Chen, Jinying & Martha S. Palmer
2009. Improving English verb sense disambiguation performance with linguistically motivated features and clear sense distinction boundaries. Language Resources and Evaluation 43:2 ► pp. 181 ff.
CHOYOUNGSOON
2018. Behavioral Profiles of Sentence Adverbs of Evaluation and Attitude. The Linguistic Association of Korea Journal 26:3 ► pp. 235 ff.
Colucci, Ilaria, Elisabetta Jezek & Vít Baisa
2020. Clustering verbal Objects: manual and automatic procedures compared. In Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020, ► pp. 128 ff.
2024. A Multiple Correspondence Analysis on the Adverbial Uses of the Chinese Color Term Bai ‘White’. In Chinese Lexical Semantics [Lecture Notes in Computer Science, 14514], ► pp. 254 ff.
Georgakopoulos, Thanasis, Eliese-Sophia Lincke, Kiki Nikiforidou & Anna Piata
2005. A Pattern Dictionary for Natural Language Processing. Revue française de linguistique appliquée Vol. X:2 ► pp. 63 ff.
Kilgarriff, Adam
2004. How Dominant Is the Commonest Sense of a Word?. In Text, Speech and Dialogue [Lecture Notes in Computer Science, 3206], ► pp. 103 ff.
Kilgarriff, Adam
2007. Word Senses. In Word Sense Disambiguation [Text, Speech and Language Technology, 33], ► pp. 29 ff.
Kokkinakis, Dimitrios
2000. Concordancing Revised or How to Aid the Recognition of New Senses in Very Large Corpora. In Natural Language Processing — NLP 2000 [Lecture Notes in Computer Science, 1835], ► pp. 370 ff.
2021. Comparison and Sense Induction of Temporal Adverbs Reng and Hai: A Corpus-Based Study. In Chinese Lexical Semantics [Lecture Notes in Computer Science, 12278], ► pp. 371 ff.
Lin, Yen-Yu & Siaw-Fong Chung
2021. A Corpus-Based Study on Two Near-Synonymous Verbs in Academic Journals: PROPOSE and SUGGEST. English Teaching & Learning 45:2 ► pp. 189 ff.
2017. Distributed Representations of Lexical Sets and Prototypes in Causal Alternation Verbs. Italian Journal of Computational Linguistics 3:1 ► pp. 25 ff.
Rajh, Ivanka & Larisa Grčić Simeunović
2019. Classification of the Combinatorial Behavior of Verbs in the Marketing Domain. In Computational and Corpus-Based Phraseology [Lecture Notes in Computer Science, 11755], ► pp. 360 ff.
2021. Review of Blanco, Marta, Hella Olbertz and Victoria Vázquez Rozas eds. 2019. Corpus y Construcciones: Perspectivas Hispánicas. (Verba: Anexo 79). Santiago de Compostela: Universidade de Santiago de Compostela. ISBN: 978-8-417-59587-6. https://dx.doi.org/10.15304/9788417595876. Research in Corpus Linguistics 9:2 ► pp. 201 ff.
Wilks, Yorick
2012. Computational Semantics Requires Computation. In Cross-Disciplinary Advances in Applied Natural Language Processing, ► pp. 1 ff.
Wu, Shuqiong
2021. A corpus-based study of the Chinese synonymous approximativesshangxia, qianhouandzuoyou. Corpus Linguistics and Linguistic Theory 17:2 ► pp. 411 ff.
Zhong, Qian & Tianqi He
2024. Profiling the Mandarin Physical Contact Verbs with ná, wò, chí and zhuā Using Collocational, Syntactic and Discourse Features. In Chinese Lexical Semantics [Lecture Notes in Computer Science, 14514], ► pp. 211 ff.
卓, 淑敏
2019. COCA Corpus-Based Study of the Near-Synonymous Verbs—Taking “Destroy”, “Damage” and “Ruin” for Reference. Modern Linguistics 07:01 ► pp. 27 ff.
徐, 文倩
2022. Review and Prospect of Semantic Research Methods of Synonyms at Home and Abroad. Modern Linguistics 10:03 ► pp. 366 ff.
이문우
2012. The use of corpus evidence for studying syntactic and semantic differences of “Hope, Want, Wish.”. Journal of the Korea English Education Society 11:2 ► pp. 41 ff.
최영주
2016. Corpus Based Collocation Analysis of the Phrasal Verb Stand Up. English21 29:2 ► pp. 379 ff.
This list is based on CrossRef data as of 31 march 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers.
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