Selection of effective contextual information for automatic synonym acquisition

Hagiwara, Masato; Ogawa, Yasushi; Toyama, Katsuhiko

doi:10.3115/1220175.1220220

Cited by 19 publications

(14 citation statements)

References 10 publications

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“…We used the WordNet similarity of words as used by Hagiwara et al [9] to evaluate our model. WordNet similarity is based on the thesaurus tree structure in WordNet.…”

Section: Wordnet Similaritymentioning

confidence: 99%

Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model

Sato

Yoshida

2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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We developed a model based on nonparametric Bayesian modeling for automatic discovery of semantic relationships between words taken from a corpus. It is aimed at discovering semantic knowledge about words in particular domains, which has become increasingly important with the growing use of text mining, information retrieval, and speech recognition. The subject-predicate structure is taken as a syntactic structure with the noun as the subject and the verb as the predicate. This structure is regarded as a graph structure. The generation of this graph can be modeled using the hierarchical Dirichlet process and the Pitman-Yor process. The probabilistic generative model we developed for this graph structure consists of subject-predicate structures extracted from a corpus. Evaluation of this model by measuring the performance of graph clustering based on WordNet similarities demonstrated that it outperforms other baseline models.

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“…We used the WordNet similarity of words as used by Hagiwara et al [9] to evaluate our model. WordNet similarity is based on the thesaurus tree structure in WordNet.…”

Section: Wordnet Similaritymentioning

confidence: 99%

Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model

Sato

Yoshida

2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…One study [8] integrated the pattern-based and distributional similarity methods to acquire lexical entailment. Another study [25] investigated the impact of contextual information selection for automatic synonym acquisition by extracting 3 kinds of contextual information, dependency, sentence cooccurrence, and proximity, from 3 different corpora. The authors proposed that while dependency relations and proximity perform relatively well by themselves, the combination of two or more kinds of contextual information gives more stable results.…”

Section: Introductionmentioning

confidence: 99%

Turkish synonym identification from multiple resources: monolingual corpus, mono/bilingual online dictionaries, and WordNet

Yıldız¹,

Di̇ri̇²,

Yıldırım³

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this study, a model is proposed to determine synonymy by incorporating several resources. The model extracts the features from monolingual online dictionaries, a bilingual online dictionary, WordNet, and a monolingual Turkish corpus. Once it has built a candidate list, it determines the synonymy for a given word by means of those features. All these resources and the approaches are evaluated. Taking all features into account and applying machine learning algorithms, the model shows good performance of F-measure with 81.4%. The study contributes to the literature by integrating several resources and attempting the first corpus-driven synonym detection system for Turkish.

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“…The first step is to represent each given expression with a set of co-occurring expressions in the relevant corpus. For instance, adjacent word n-grams [7,43,47], nominal arguments of verb phrases [13,40,52,54,55], modifiers and modified words [26,56], and even indirect dependencies [27] have been used. Then, the weight for each feature is adjusted.…”

Section: Automatic Paraphrase Acquisition From Monolingual Corporamentioning

confidence: 99%

Expanding Paraphrase Lexicons by Exploiting Generalities

Fujita

Isabelle

2018

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Techniques for generating and recognizing paraphrases, i.e., semantically equivalent expressions, play an important role in a wide range of natural language processing tasks. In the last decade, the task of automatic acquisition of subsentential paraphrases, i.e., words and phrases with (approximately) the same meaning, has been drawing much attention in the research community. The core problem is to obtain paraphrases of high quality in large quantity. This article presents a method for tackling this issue by systematically expanding an initial seed lexicon made up of high-quality paraphrases. This involves automatically capturing morphosemantic and syntactic generalizations within the lexicon and using them to leverage the power of large-scale monolingual data. Given an input set of paraphrases, our method starts by inducing paraphrase patterns that constitute generalizations over corresponding pairs of lexical variants, such as "amending" and "amendment," in a fully empirical way. It then searches large-scale monolingual data for new paraphrases matching those patterns. The results of our experiments on English, French, and Japanese demonstrate that our method manages to expand seed lexicons by a large multiple. Human evaluation based on paraphrase substitution tests reveals that the automatically acquired paraphrases are also of high quality.

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Selection of effective contextual information for automatic synonym acquisition

Cited by 19 publications

References 10 publications

Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model

Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model

Turkish synonym identification from multiple resources: monolingual corpus, mono/bilingual online dictionaries, and WordNet

Expanding Paraphrase Lexicons by Exploiting Generalities

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