Quality assessment of large scale knowledge resources

Cuadros, Montse; Rigau, Germán

doi:10.3115/1610075.1610149

Cited by 27 publications

(20 citation statements)

References 5 publications

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“…When we have more than eight to thirteen relation edges that enable the connectivity from a specific sense to other senses in the lexicon, the performance grows noticeably. The trend shown in the graph corroborates the apparently trivial observation that the availability of large knowledge bases (even (semi)automatically acquired) tends to improve the disambiguation performance (Cuadros and Rigau 2006). Moreover, it shows that the improvement can be really significant, with an increase of several tenths of points in terms of both precision and recall.…”

Section: #6supporting

confidence: 79%

A structural approach to the automatic adjudication of word sense disagreements

Navigli¹

2008

Nat. Lang. Eng.

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The semantic annotation of texts with senses from a computational lexicon is a complex and often subjective task. As a matter of fact, the fine granularity of the WordNet sense inventory [Fellbaum, Christiane (ed.). 1998. WordNet: An Electronic Lexical Database MIT Press], a de facto standard within the research community, is one of the main causes of a low inter-tagger agreement ranging between 70% and 80% and the disappointing performance of automated fine-grained disambiguation systems (around 65% state of the art in the Senseval-3 English all-words task). In order to improve the performance of both manual and automated sense taggers, either we change the sense inventory (e.g. adopting a new dictionary or clustering WordNet senses) or we aim at resolving the disagreements between annotators by dealing with the fineness of sense distinctions. The former approach is not viable in the short term, as wide-coverage resources are not publicly available and no large-scale reliable clustering of WordNet senses has been released to date. The latter approach requires the ability to distinguish between subtle or misleading sense distinctions. In this paper, we propose the use of structural semantic interconnections -a specific kind of lexical chains -for the adjudication of disagreed sense assignments to words in context. The approach relies on the exploitation of the lexicon structure as a support to smooth possible divergencies between sense annotators and foster coherent choices. We perform a twofold experimental evaluation of the approach applied to manual annotations from the SemCor corpus, and automatic annotations from the Senseval-3 English all-words competition. Both sets of experiments and results are entirely novel: structural adjudication allows to improve the state-of-the-art performance in all-words disambiguation by 3.3 points (achieving a 68.5% F1-score) and attains figures around 80% precision and 60% recall in the adjudication of disagreements from human annotators.

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Section: #6supporting

confidence: 79%

A structural approach to the automatic adjudication of word sense disagreements

Navigli¹

2008

Nat. Lang. Eng.

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“…to determine the senses of words in context. However, it has been shown in (Cuadros and Rigau, 2006) that the amount of lexical and semantic information contained in such resources is typically insufficient for high-performance WSD. Much work has been presented to automatically extend existing resources, including automatically linking Wikipedia to WordNet to include full use of the first WordNet sense heuristic (Suchanek et al, 2008), a graph-based mapping of Wikipedia categories to WordNet synsets (Ponzetto and Navigli, 2009), and automatically mapping Wikipedia pages to WordNet synsets (Ponzetto and Navigli, 2010).…”

Section: Knowledge-based Wsdmentioning

confidence: 99%

A Unified Model for Word Sense Representation and Disambiguation

Chen¹,

Liu²,

Sun³

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

218

186

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Most word representation methods assume that each word owns a single semantic vector. This is usually problematic because lexical ambiguity is ubiquitous, which is also the problem to be resolved by word sense disambiguation. In this paper, we present a unified model for joint word sense representation and disambiguation, which will assign distinct representations for each word sense. 1 The basic idea is that both word sense representation (WS-R) and word sense disambiguation (WS-D) will benefit from each other: (1) highquality WSR will capture rich information about words and senses, which should be helpful for WSD, and (2) high-quality WSD will provide reliable disambiguated corpora for learning better sense representations. Experimental results show that, our model improves the performance of contextual word similarity compared to existing WSR methods, outperforms stateof-the-art supervised methods on domainspecific WSD, and achieves competitive performance on coarse-grained all-words WSD.

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“…It is often an objective in itself in natural language processing, but at the same time it is an essential component in a variety of applications (for example, in question-answering). Remarkably, large-scale conceptual networks have been applied and evaluated in the literature as part of the word sense disambiguation and induction tasks (e.g., Navigli and Lapata [2010], Cuadros and Rigau [2006], Navigli [2009b], , Pantel and Lin [2002]). NLP techniques have also been used to extract semantic networks (Mintz et al [2009], Snow et al [2006], Richardson et al [1998]), for example, for text summarisation (Lin and Hovy [2000]) and adapting general lexicons to specific domains (Toumouth et al [2006], ), and so on.…”

Section: Natural Language Processingmentioning

confidence: 99%

Automatically structuring domain knowledge from text: An overview of current research

Clark

Kim

Kruschwitz

et al. 2012

Information Processing & Management

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This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably as a useful tool within natural language processing, information retrieval and semantic web technology. Inspired by the ubiquitous propagation of domain model structures that are emerging in several research disciplines, we give an overview of the current research landscape and some techniques and approaches. We will also discuss trade-offs between different approaches and point to some recent trends.

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Quality assessment of large scale knowledge resources

Cited by 27 publications

References 5 publications

A structural approach to the automatic adjudication of word sense disagreements

A structural approach to the automatic adjudication of word sense disagreements

A Unified Model for Word Sense Representation and Disambiguation

Automatically structuring domain knowledge from text: An overview of current research

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