Idiomatic object usage and support verbs

Tapanainen, Pasi; Piitulainen, Jussi; Järvinen, Timo

doi:10.3115/980691.980779

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…A number of approaches focus on the extraction of specific types of collocations such as support verb constructions, svcs (Grefenstette and Teufel, 1995;Tapanainen et al, 1998). 18 They classify, to an extent, binary word combinations into svcs and "non-relevant" combinations.…”

Section: Related Workmentioning

confidence: 99%

Towards automatic fine-grained semantic classification of verb-noun collocations

Wanner

2004

Nat. Lang. Eng.

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Plain lists of collocations as provided to date by most approaches to automatic acquisition of collocations from corpora are useful as a resource for dictionary construction. However, their use is rather limited in the case of NLP-applications such as Text Generation, Machine Translation and Text Summarization if not enriched by information on the grammatical function of the collocation elements and by information on the semantics of the collocations as multiword units. In this article, we describe an approach to a fine-grained classification of verbnoun bigrams according to a semantically motivated typology of collocations and illustrate this with Spanish material. The typology of collocations that underlies our classification is based on verb-noun Lexical Functions (lfs) from the Explanatory Combinatorial Lexicology. In the first stage of the approach, the program learns the semantic features of each lf from training data. In the second stage, it examines the semantic features of verb-noun candidate bigrams and compares them with the features of all the lfs taken into account. A candidate whose features are sufficiently similar to those of a specific lf is considered to be an instance of this lf. The semantic features of both the training material and the candidate bigrams are derived from the hyperonymy hierarchies provided by the EuroWordNet. In the experiments carried out to validate the approach, we achieved an average f-score of about 70%.

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Section: Related Workmentioning

confidence: 99%

Towards automatic fine-grained semantic classification of verb-noun collocations

Wanner

2004

Nat. Lang. Eng.

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“…To the best of our knowledge, no other work in the literature takes into account both specific and general properties of non-compositional MWEs for their identification and hence unlike the presented model, most available models suffer from either a low precision or a low recall. Some of the earliest works on non-compositional MWEs include Tapanainen et al [1998] who propose a method to identify non-compositional verb-object collocations 2 based on the semantic asymmetry of verb-object relation and Lin [1999] which was discussed earlier. Baldwin et al [2003] present a method that decides about the non-compositionality of English noun compounds and verb-particle constructions by comparing the vectors of their components against the vector of the phrase.…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Model for Representing Semantic Non-compositionality

Farahmand¹

2019

Preprint

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Semantically non-compositional phrases constitute an intriguing research topic in Natural Language Processing. Semantic non-compositionality -the situation when the meaning of a phrase cannot be derived from the meaning of its components, is the main characteristic of such phrases, however, they bear other characteristics such as high statistical association and non-substitutability. In this work, we present a model for identifying non-compositional phrases that takes into account all of these characteristics. We show that the presented model remarkably outperforms the existing models of identifying non-compositional phrases that mostly focus only on one of these characteristics.

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“…Some of them are Frequency, Point-wise mutual information (Church and Hanks, 1989), Distributed frequency of object (Tapanainen et al, 1998), Distributed frequency of object using verb information (Venkatapathy and Joshi, 2005), Similarity of object in verbobject pair using the LSA model (Baldwin et al, 2003), (Venkatapathy and Joshi, 2005) and Lexical and Syntactic fixedness (Fazly and Stevenson, 2006). These features have largely been evaluated by the correlation of the compositionality value predicted by these measures with the gold standard value suggested by human judges.…”

Section: Related Workmentioning

confidence: 99%

“…Some of these are mutual information (Church and Hanks, 1989), distributed frequency (Tapanainen et al, 1998) and Latent Semantic Analysis (LSA) model (Baldwin et al, 2003). Even though, these measures have been shown to represent compositionality quite well, compositionality itself has not been shown to be useful in any application yet.…”

Section: Introductionmentioning

confidence: 99%

Using information about multi-word expressions for the word-alignment task

Venkatapathy

Joshi

2006

Proceedings of the Workshop on Multiword Expressions Identifying and Exploiting Underlying Properties - MWE '06

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It is well known that multi-word expressions are problematic in natural language processing. In previous literature, it has been suggested that information about their degree of compositionality can be helpful in various applications but it has not been proven empirically. In this paper, we propose a framework in which information about the multi-word expressions can be used in the word-alignment task. We have shown that even simple features like point-wise mutual information are useful for word-alignment task in English-Hindi parallel corpora. The alignment error rate which we achieve (AER = 0.5040) is significantly better (about 10% decrease in AER) than the alignment error rates of the state-of-art models (Och and Ney, 2003) (Best AER = 0.5518) on the English-Hindi dataset.

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Idiomatic object usage and support verbs

Cited by 7 publications

References 6 publications

Towards automatic fine-grained semantic classification of verb-noun collocations

Towards automatic fine-grained semantic classification of verb-noun collocations

A Multivariate Model for Representing Semantic Non-compositionality

Using information about multi-word expressions for the word-alignment task

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