Discriminating among word senses using McQuitty's similarity analysis

Purandare, Amruta

doi:10.3115/1073416.1073420

Cited by 6 publications

(6 citation statements)

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“…Purandare and Pedersen (e.g., [12], [13]) have developed methods of clustering multiple occurrences of a given word into senses based on their contextual similarity. In this paper we adapt those techniques to the problem of name discrimination.…”

Section: Discrimination By Clustering Similar Contextsmentioning

confidence: 99%

“…In this paper we show how the unsupervised word sense discrimination methods of Purandare and Pedersen (e.g., [12], [13]) can be applied to the problem of name discrimination. We begin with a summary of related work on the problem of name discrimination, and then describe our approach, which is based on clustering second-order context vectors whose dimensions have been reduced by Singular Value Decomposition (SVD).…”

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Name Discrimination by Clustering Similar Contexts

Pedersen

Purandare

Kulkarni

2005

Computational Linguistics and Intelligent Text Processing

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Abstract. It is relatively common for different people or organizations to share the same name. Given the increasing amount of information available online, this results in the ever growing possibility of finding misleading or incorrect information due to confusion caused by an ambiguous name. This paper presents an unsupervised approach that resolves name ambiguity by clustering the instances of a given name into groups, each of which is associated with a distinct underlying entity. The features we employ to represent the context of an ambiguous name are statistically significant bigrams that occur in the same context as the ambiguous name. From these features we create a co-occurrence matrix where the rows and columns represent the first and second words in bigrams, and the cells contain their log-likelihood scores. Then we represent each of the contexts in which an ambiguous name appears with a second order context vector. This is created by taking the average of the vectors from the co-occurrence matrix associated with the words that make up each context. This creates a high dimensional "instance by word" matrix that is reduced to its most significant dimensions by Singular Value Decomposition (SVD). The different "meanings" of a name are discriminated by clustering these second order context vectors with the method of Repeated Bisections. We evaluate this approach by conflating pairs of names found in a large corpus of text to create ambiguous pseudo-names. We find that our method is significantly more accurate than the majority classifier, and that the best results are obtained by having a small amount of local context to represent the instance, along with a larger amount of context for identifying features, or vice versa.

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Section: Discrimination By Clustering Similar Contextsmentioning

confidence: 99%

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Name Discrimination by Clustering Similar Contexts

Pedersen

Purandare

Kulkarni

2005

Computational Linguistics and Intelligent Text Processing

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“…In the case of [3], the discovering of word senses is a side effect of the clustering algorithm, Cluster By Committee, used for building classes of words: as a word can belong to several classes, each of them can be considered as one of its senses. In [4], [5] and [6], Purandare etc. represent each instance of a target word by a set of features that occur in its neighborhood and applies an unsupervised clustering algorithm to all its instances.…”

Section: Related Workmentioning

confidence: 99%

Discovering Chinese Concept-In-Corpus

Chen

Zheng

Zhao

2008

2008 International Conference on Machine Learning and Cybernetics

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Concept is the basic of knowledge. A concept consists of a connotation and an extension. The paper comes up with a concept of Concept-In-Corpus which is a special kind of formal concept, and presents a discovering algorithm called FCWFT (Filtering Concept-word Based on Feature-tree) which automatically mine the connotation and the extension for a Chinese Concept-In-Corpus from corpus in Chinese.Our work is the first one attempting to mine formal Concepts from free texts in the area of Natural Language Processing. We test the algorithm with a large scale corpus. The result is encouraging.

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“…The method of clustering similar contexts developed by Purandare and Pedersen is well described elsewhere (e.g., [9], [10]) and is implemented in the freely available SenseClusters package 1 . In this paper we employ one variation of their general approach, which results in a second order co-occurrence representation of the contexts to be clustered.…”

Section: Discrimination By Clustering Similar Contextsmentioning

confidence: 99%

An Unsupervised Language Independent Method of Name Discrimination Using Second Order Co-occurrence Features

Pedersen

Kulkarni

Angheluta

et al. 2006

Computational Linguistics and Intelligent Text Processing

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Abstract. Previous work by Pedersen, Purandare and Kulkarni (2005) has resulted in an unsupervised method of name discrimination that represents the context in which an ambiguous name occurs using second order co-occurrence features. These contexts are then clustered in order to identify which are associated with different underlying named entities. It also extracts descriptive and discriminating bigrams from each of the discovered clusters in order to serve as identifying labels. These methods have been shown to perform well with English text, although we believe them to be language independent since they rely on lexical features and use no syntactic features or external knowledge sources. In this paper we apply this methodology in exactly the same way to Bulgarian, English, Romanian, and Spanish corpora. We find that it attains discrimination accuracy that is consistently well above that of a majority classifier, thus providing support for the hypothesis that the method is language independent.

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Discriminating among word senses using McQuitty's similarity analysis

Cited by 6 publications

References 9 publications

Name Discrimination by Clustering Similar Contexts

Name Discrimination by Clustering Similar Contexts

Discovering Chinese Concept-In-Corpus

An Unsupervised Language Independent Method of Name Discrimination Using Second Order Co-occurrence Features

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