Clustering Web Search Results with Maximum Spanning Trees

Marco, A. Di; Navigli, Roberto

doi:10.1007/978-3-642-23954-0_20

Cited by 12 publications

(8 citation statements)

References 44 publications

(36 reference statements)

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“…-Within an application: a further way to evaluate the output of WSI is within an application. A paradigmatic example of this kind is the application of WSI to Web search result clustering [54,13], where WSI techniques have been shown to consistently surpass non-semantic state-of-the-art systems.…”

Section: Discussionmentioning

confidence: 99%

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A Quick Tour of Word Sense Disambiguation, Induction and Related Approaches

Navigli

2012

SOFSEM 2012: Theory and Practice of Computer Science

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Abstract. Word Sense Disambiguation (WSD) and Word Sense Induction (WSI) are two fundamental tasks in Natural Language Processing (NLP), i.e., those of, respectively, automatically assigning meaning to words in context from a predefined sense inventory and discovering senses from text for a given input word. The two tasks have generally been hard to perform with high accuracy. However, today innovations in approach to WSD and WSI are promising to open up many interesting new horizons in NLP and Information Retrieval applications. This paper is a quick tour on how to start doing research in this exciting field and suggests the hottest topics to focus on.

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Section: Discussionmentioning

confidence: 99%

“…We can already see the first evidence that text understanding techniques improve the state of the art in fields such as search result clustering [54,13] and lexicography [20]. However, much work is still needed to prove that a proper injection of semantics into real-world applications is always beneficial.…”

Section: Discussionmentioning

confidence: 99%

A Quick Tour of Word Sense Disambiguation, Induction and Related Approaches

Navigli

2012

SOFSEM 2012: Theory and Practice of Computer Science

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“…Recently, Navigli and Crisafulli () and Di Marco and Navigli () present in their papers a novel approach to web search result clustering, based on the automatic discovery of word senses from raw text. WSI is performed in order to dynamically acquire an inventory of senses of the input query.…”

Section: Related Workmentioning

confidence: 99%

Word Sense Induction with Closed Frequent Termsets

Kozłowski

Rybiński

2016

Computational Intelligence

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The article is devoted to the problem of word sense induction. We propose a method for inducing senses from a raw text corpus. The proposed sense induction algorithm (called SenseSearcher, or SnS) is based on closed frequent sets, and as a result, it provides a multilevel sense representation. To a large extent, it is a knowledge-poor approach, as it does not need any kind of structured knowledge base about senses and there is no deep language knowledge embedded. By discovering a hierarchy of senses, the algorithm enables identifying subsenses (finegrained senses). SnS discovers not only frequent (dominating) senses but also infrequent ones (dominated). The method was evaluated in two main areas: lexicography and information retrieval. With the use of the SnS algorithm, we provide a tool able to induce from a textual corpus a structure of senses, with a varying number of granularity levels. In the area of information retrieval, SnS can be used for clustering search result, according to the discovered senses. The experiments have shown that SnS performs better than the methods participating in the SemEval2013 WSI Task 11 competition, and most of the known search result clustering methods. 1 The numbers include all parts of speech (including verbs, which are more ambiguous by nature); we cite them because finding novel meanings of any type is an important issue in NLP tasks.

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“…This article extends previous conference work (Navigli and Crisafulli 2010;Di Marco and Navigli 2011) by performing a novel, in-depth study of the interactions between different corpora and several different WSI algorithms, including novel ones, within the same framework, and, additionally, by providing a comparison with a stateof-the-art search result clustering engine.…”

Section: Introductionmentioning

confidence: 95%

“…r We present novel versions of previously proposed WSI graph-based algorithms, namely, SquaT++ and Balanced Maximum Spanning Tree (B-MST) (the former is an enhancement of the original SquaT algorithm [Navigli and Crisafulli 2010], and the latter is a variant of MST [Di Marco and Navigli 2011] that produces more balanced clusters).…”

Section: Introductionmentioning

confidence: 99%

Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction

Marco

Navigli

2013

Computational Linguistics

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109

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Web search result clustering aims to facilitate information search on the Web. Rather than the results of a query being presented as a flat list, they are grouped on the basis of their similarity and subsequently shown to the user as a list of clusters. Each cluster is intended to represent a different meaning of the input query, thus taking into account the lexical ambiguity (i.e., polysemy) issue. Existing Web clustering methods typically rely on some shallow notion of textual similarity between search result snippets, however. As a result, text snippets with no word in common tend to be clustered separately even if they share the same meaning, whereas snippets with words in common may be grouped together even if they refer to different meanings of the input query.In this article we present a novel approach to Web search result clustering based on the automatic discovery of word senses from raw text, a task referred to as Word Sense Induction. Key to our approach is to first acquire the various senses (i.e., meanings) of an ambiguous query and then cluster the search results based on their semantic similarity to the word senses induced. Our experiments, conducted on data sets of ambiguous queries, show that our approach outperforms both Web clustering and search engines.

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Clustering Web Search Results with Maximum Spanning Trees

Cited by 12 publications

References 44 publications

A Quick Tour of Word Sense Disambiguation, Induction and Related Approaches

A Quick Tour of Word Sense Disambiguation, Induction and Related Approaches

Word Sense Induction with Closed Frequent Termsets

Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction

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