Noun classification from predicate-argument structures

Hindle, Don

doi:10.3115/981823.981857

Cited by 342 publications

(257 citation statements)

References 6 publications

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“…-Hindle's method: The method described in [7] is used. Whereas he deals only with subjects and objects as verb-noun co-occurrence, we used all the kinds of co-occurrence mentioned in Sect.…”

Section: Comparison Experiments With Conventional Methodsmentioning

confidence: 99%

“…To acquire synonyms automatically, contextual features of words, such as co-occurrence and modification are extracted from large corpora and often used. Hindle [7], for example, extracted verb-noun relationships of subjects/objects and their predicates from a corpus and proposed a method to calculate similarity of two words based on their mutual information. Although methods based on such raw co-occurrences are simple yet effective, in a naive implementation some problems arise: namely, noises and sparseness.…”

Section: Introductionmentioning

confidence: 99%

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PLSI Utilization for Automatic Thesaurus Construction

Hagiwara

Ogawa

Toyama

2005

Lecture Notes in Computer Science

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Abstract. When acquiring synonyms from large corpora, it is important to deal not only with such surface information as the context of the words but also their latent semantics. This paper describes how to utilize a latent semantic model PLSI to acquire synonyms automatically from large corpora. PLSI has been shown to achieve a better performance than conventional methods such as tf·idf and LSI, making it applicable to automatic thesaurus construction. Also, various PLSI techniques have been shown to be effective including: (1) use of Skew Divergence as a distance/similarity measure; (2) removal of words with low frequencies, and (3) multiple executions of PLSI and integration of the results.

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Section: Comparison Experiments With Conventional Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PLSI Utilization for Automatic Thesaurus Construction

Hagiwara

Ogawa

Toyama

2005

Lecture Notes in Computer Science

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show abstract

“…Linguistic models and machine learning techniques are used in automatically detecting relations, patterns, and structures in textual data at various granularities. At the word level, relationships among lexical items can be detected by using grammatical knowledge and statistical methods on large text corpora (Hindle, 1990;Hearst 1998). Moving up to the sentence/discourse level, based on theories of rhetorical and discourse structures (Mann and Thompson, 1988;Polanyi, 1988;Grosz et al, 1995), much work has been done on automatically detecting relationships between sentences and other discourse units (Marcu and Echibabi, 2002;Burstein et al 2003;Chan, 2004).…”

Section: Detecting Relationships In Textual Datamentioning

confidence: 99%

Using Category Information for Relationship Exploration in Textual Data

Yan

Furnas

Walstrum

2006

Proc of Assoc for Info

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“…The similarity measure is a weighted Tanimoto measure, a version of which was also used by Grefenstette (1992Grefenstette ( , 1994. Word association is measured by mutual information, following earlier work on word similarity by Hindle (1990).…”

Section: Statistical Similarity and Clustering For Disambiguation Andmentioning

confidence: 99%

Untitled

1999

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Abstract. In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations "eat a peach" and "eat a beach" is more likely. Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in any given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on "most similar" words.We describe probabilistic word association models based on distributional word similarity, and apply them to two tasks, language modeling and pseudo-word disambiguation. In the language modeling task, a similarity-based model is used to improve probability estimates for unseen bigrams in a back-off language model. The similaritybased method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error.We also compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency to avoid giving too much weight to easy-to-disambiguate high-frequency configurations. The similaritybased methods perform up to 40% better on this particular task.

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Noun classification from predicate-argument structures

Cited by 342 publications

References 6 publications

PLSI Utilization for Automatic Thesaurus Construction

PLSI Utilization for Automatic Thesaurus Construction

Using Category Information for Relationship Exploration in Textual Data

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