Corpus-based statistical sense resolution

Leacock, Claudia; Towell, Geoffrey G.; Voorhees, Ellen M.

doi:10.3115/1075671.1075730

Cited by 82 publications

(67 citation statements)

References 8 publications

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“…Obviously, large values of window sizes capture dependencies at longer range but also dilute the effect of the words closer to the term. Leacock et al [28] used a window size of 50, while Yarowsky [29] argued that a small window size of 3 or 4 had better performance. A small window size has an advantage of requiring less system space and running time [30].…”

Section: Data Acquisition and Feature Extractionmentioning

confidence: 99%

Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

Zhong

Wang

et al. 2018

Mathematical Problems in Engineering

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In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the Big Data records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.

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Section: Data Acquisition and Feature Extractionmentioning

confidence: 99%

Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

Zhong

Wang

et al. 2018

Mathematical Problems in Engineering

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“…While n-grams perform well in partof-speech tagging and speech processing, they require a fixed interdependency structure that is inappropriate for the broad class of contextual features used in word-sense disambiguation. However, the Naive Bayes classifier has been found to perform well for word-sense disambiguation both here and in a variety of other works (e.g., (Bruce and Wiebe, 1994a), (Gale et al, 1992), (Leacock et al, 1993), and (Mooney, 1996)). …”

Section: Related Workmentioning

confidence: 99%

Sequential model selection for word sense disambiguation

Pedersen

Brucet

Wiebe

1997

Proceedings of the Fifth Conference on Applied Natural Language Processing -

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Statistical models of word-sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted in order to find the model that best characterizes the interactions among features. This paper expands existing model selection methodology and presents the first comparative study of model selection search strategies and evaluation criteria when applied to the problem of building probabilistic classifiers for word-sense disambiguation.

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“…In particular, we use subsets of the line data (Leacock et al, 1993) and the English lexical sample data from the SENSEVAL-2 comparative exercise among word sense disambiguation systems (Edmonds and Cotton, 2001). …”

Section: Experimental Methodologymentioning

confidence: 99%

Discriminating among word senses using McQuitty's similarity analysis

Purandare

2003

Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Lang

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This paper presents an unsupervised method for discriminating among the senses of a given target word based on the context in which it occurs. Instances of a word that occur in similar contexts are grouped together via McQuitty's Similarity Analysis, an agglomerative clustering algorithm. The context in which a target word occurs is represented by surface lexical features such as unigrams, bigrams, and second order co-occurrences. This paper summarizes our approach, and describes the results of a preliminary evaluation we have carried out using data from the SENSEVAL-2 English lexical sample and the line corpus.

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Corpus-based statistical sense resolution

Cited by 82 publications

References 8 publications

Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

Sequential model selection for word sense disambiguation

Discriminating among word senses using McQuitty's similarity analysis

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