Spatial iconicity in two English verb classes

2003 ◽  
pp. 63-76
Author(s):  
Axel Hübler
Keyword(s):  
Verb Classes ◽  
English Verb

scholarly journals FrameNet's Frames vs. Levin's Verb Classes

2002 ◽  
Vol 28 (1) ◽  
pp. 27 ◽  
Author(s):  
Collin F. Baker ◽  
Josef Ruppenhofer
Keyword(s):  
Semantic Similarity ◽  
Preliminary Investigation ◽  
Verb Classes ◽  
Conceptual Structures ◽  
Semantic Frames ◽  
English Verb

The classification of verbs in Levin's (1993) English Verb Classes and Alternations: A preliminary Investigation, on the basis of both intuitive semantic grouping and their participation in valence alternations, is often used by the NLP community as evidence of the semantic similarity of verbs (Jing & McKeown 1998; Lapata & Brew 1999; Kohl et al. 1998). In this paper, we compare the Levin classification with the work of the FrameNet project (Fillmore & Baker 2001), where words (not just verbs) are grouped according to the conceptual structures (frames) that underlie them and their combinatorial patterns are inductively derived from corpus evidence. This means that verbs grouped together in FrameNet (FN) might be semantically similar but have different (or no) alternations, and that verbs which share the same alternation might be represented in two different semantic frames.

An Analysis on Newly Introduced English Verb in Oxford English Dictionary in 2016-2018: Levin’s Taxonomy of Verb Classification and Verbnet

2021 ◽  
Vol 5 (4) ◽  
pp. 26
Author(s):  
Kamilah Zainuddin ◽  
Noor Asmaa' Hussein
Keyword(s):  
Word Meaning ◽  
The Body ◽  
Oxford English Dictionary ◽  
Verb Classes ◽  
Body Of Knowledge ◽  
Main Verb ◽  
Verb Classification ◽  
Verb Lexicon ◽  
The Given ◽  
English Verb

Levin (1993) argues that the behaviour of verbs is determined by word meaning which is directly linked to the expression and interpretation of its argument. Based on this statement, Levin classified verbs into 48 classes and VerbNet, an English verb lexicon was created based on the extension of Levin’s taxonomy of verb classification (VC). Therefore, this study presents the classification of the updated English verbs of 2016 to 2018 in the online version of the Oxford English Dictionary (OED) to examine the relevance of Levin’s VC and VerbNet in the recently added repertoire of verbs. In Addition, the study seeks to find out the new verb classes introduced in this study. This study uses a mixed-method to identify and classify the verbs. To conclude, this study found that both classifications were applicable and relevant in 14 new main verb classes introduced to classify the verbs that did not belong to Levin’s VC and VerbNet. Hence, this contributes to the body of knowledge as the newly introduced verb classes could be used based on the given semantic and syntactic conditions.

scholarly journals English Verb Classes and Alternations: A Preliminary Investigation

Language ◽  
1995 ◽  
Vol 71 (1) ◽  
pp. 144
Author(s):  
Carol Tenny ◽  
Beth Levin
Keyword(s):  
Preliminary Investigation ◽  
Verb Classes ◽  
English Verb

scholarly journals Old English verbs of prohibition. Grammatical behaviour and class membership

2019 ◽  
Vol 24 (1) ◽  
pp. 1
Author(s):  
Ana Elvira Ojanguren López
Keyword(s):  
Theoretical Model ◽  
Old English ◽  
Class Membership ◽  
Verb Classes ◽  
Role And Reference Grammar ◽  
Reference Grammar ◽  
English Verb

The aim of this article is to determine if the Old English verbs bewerian, forbēodan, foresacan, forwiernan, stīeran and tōcweþān constitute a unified class of prohibition. The theoretical model is provided by the framework of verb classes and alternations, as well as by Role and Reference Grammar. Class membership requires not only similar meaning components but also shared grammatical behaviour. While bewerian, forbēodan and forwyrnan are found in three syntactic configurations, and in the Nominalisation and Undergoer alternations, foresacan, stīeran and tōcweþān occur in one syntactic configuration only and do not take part in these alternations. The main conclusion of this article is that these verbs do not show a similar grammatical behaviour and, therefore, cannot be said to represent a consistent verbal class.Keywords: Old English; verb classes; alternations; Role and Reference Grammar

scholarly journals Towards the Quantitative Approach to Studying Evolution of English Verb Paradigm

Nordlyd ◽  
10.7557/12.18 ◽  
2004 ◽  
Vol 31 (1) ◽  
Author(s):  
Maria Glushko
Keyword(s):  
Quantitative Approach ◽  
Quantitative Investigation ◽  
Verb Classes ◽  
English Verb

The article focuses on corpus quantitative investigation of English diachronic morphology with respect to irregularregular verb classes. General tendencies of the evolution of English verb paradigm are analyzed. Three groups of English verbs have been observed: irregular and regular ones plus goup of verbs on the stage of transition from irregular category into regular one, i.e. possessing both types of conjugation forms. A considerable amount of e-texts has been processed and correspondence of these three verb classes has been revealed. The results reveal existence of certain correlation between the frequency dynamics of the verb and its belonging to irregular or regular conjugation paradigm.

Danish Motion Verbs: Syntactic Alternations and the Hypothesis of Semantic Determination

1997 ◽  
Vol 20 (1) ◽  
pp. 65-90
Author(s):  
Bolette Sanford Pederson
Keyword(s):  
Basic Assumption ◽  
Motion Verbs ◽  
Verb Classes ◽  
Core Meaning ◽  
English Verb

Danish motion verbs provide a good candidate for testing the hypothesis of semantic determination on Danish, a hypothesis most recently discussed by Levin (1993) in her description of English verb classes and alternations. A set of core meaning components for motion verbs is identified and a taxonomy of Danish motion verbs is produced. With the basic assumption that a word's meaning determines its syntactic potential, this paper gives an account of the syntactic alternations that Danish motion verbs can undergo, and exposes a more or less transparent relation between their syntax and semantics.

Learning Verb Classes in an Incremental Model

2014 ◽  
Author(s):  
Libby Barak ◽  
Afsaneh Fazly ◽  
Suzanne Stevenson
Keyword(s):  
Verb Classes ◽  
Incremental Model

scholarly journals BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Olga Majewska ◽  
Charlotte Collins ◽  
Simon Baker ◽  
Jari Björne ◽  
Susan Windisch Brown ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Model Performance ◽  
Representation Learning ◽  
Verb Classes ◽  
Expert Annotation ◽  
Biomedical Texts ◽  
Time Required ◽  
Verb Meaning

Abstract Background Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-related knowledge have been shown to boost model performance in different Natural Language Processing (NLP) tasks where accurate handling of verb meaning and behaviour is critical. The costliness and time required for manual lexicon construction has been a major obstacle to porting the benefits of such resources to NLP in specialised domains, such as biomedicine. To address this issue, we combine a neural classification method with expert annotation to create BioVerbNet. This new resource comprises 693 verbs assigned to 22 top-level and 117 fine-grained semantic-syntactic verb classes. We make this resource available complete with semantic roles and VerbNet-style syntactic frames. Results We demonstrate the utility of the new resource in boosting model performance in document- and sentence-level classification in biomedicine. We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class. The BioVerbNet knowledge-aware embeddings surpass the non-specialised baseline by a significant margin on both tasks. Conclusion This work introduces the first large, annotated semantic-syntactic classification of biomedical verbs, providing a detailed account of the annotation process, the key differences in verb behaviour between the general and biomedical domain, and the design choices made to accurately capture the meaning and properties of verbs used in biomedical texts. The demonstrated benefits of leveraging BioVerbNet in text classification suggest the resource could help systems better tackle challenging NLP tasks in biomedicine.

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