Disambiguation of proper names in text

Wacholder, Nina; Ravin, Yael; Choi, Misook

doi:10.3115/974557.974587

Cited by 115 publications

(60 citation statements)

References 8 publications

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“…al. [15], who developed an integrated approach to identifying named entities and resolving any ambiguities that might be present based on knowledge of co-occurring names in the context, and a database of known names.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%

Name Discrimination by Clustering Similar Contexts

Pedersen

Purandare

Kulkarni

2005

Computational Linguistics and Intelligent Text Processing

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“…They clustered all references in documents corresponding to the same entity by applying the vector cosine similarity measure. In [10], a similar but refined method was proposed by integrating context similarity into cross-document coreference heuristics previously developed in [11] that simply matches strings and checks entities types. The results obtained in that system were encouraging.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Matching named entities with the aid of Wikipedia

Bawakid¹,

Oussalah²,

Afzal³

et al. 2015

Turk J Elec Eng & Comp Sci

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Abstract:In this paper we propose a novel framework using features extracted from Wikipedia for the task of matching named entities. We present how the employed features are extracted from Wikipedia and how its structure is utilized. We describe how a term-concepts table constructed from Wikipedia and the redirect links are integrated in the framework. In addition, the internal links within Wikipedia along with the categories structure are also used to compute the relatedness between concepts. We evaluate the built framework and report its performance in the applications of word sense disambiguation and named entities matching. The system performance is compared against other learning-based stateof-the-art systems and its reported results are found to be competitive. We also present a method in this paper for named entities matching that is context-independent and compare its results with those of other systems.

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“…We used a named entity tagger (Wacholder et al, 1997) to collect all such information for every person. The processed references to the same people across documents were aligned using the named entity tagger canonic name, resulting in tables similar to those shown in figure 1.…”

Section: Including Parentheticalsmentioning

confidence: 99%

Syntactic Simplification for Improving Content Selection in Multi-Document Summarization

Siddharthan¹,

Nenkova²,

McKeown³

2004

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In this paper, we explore the use of automatic syntactic simplification for improving content selection in multi-document summarization. In particular, we show how simplifying parentheticals by removing relative clauses and appositives results in improved sentence clustering, by forcing clustering based on central rather than background information. We argue that the inclusion of parenthetical information in a summary is a reference-generation task rather than a content-selection one, and implement a baseline reference rewriting module. We perform our evaluations on the test sets from the 2003 and 2004 Document Understanding Conference and report that simplifying parentheticals results in significant improvement on the automated evaluation metric Rouge.

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Disambiguation of proper names in text

Cited by 115 publications

References 8 publications

Name Discrimination by Clustering Similar Contexts

Name Discrimination by Clustering Similar Contexts

Matching named entities with the aid of Wikipedia

Syntactic Simplification for Improving Content Selection in Multi-Document Summarization

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