A Supervised Method of Feature Weighting for Measuring Semantic Relatedness

Kennedy, Alistair; Śzpakowicz, Stan

doi:10.1007/978-3-642-21043-3_27

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…i,j , y (2) i,j+1 , x, j) , (10) where {f (1) g } is a set of features for advice-revealing sentence extraction (see Table 2), {f (2) m } is a set of features for context sentence extraction (see Section 5.1), and {λ (1) g } and {λ (2) m } are the respective weights for each of two feature sets. In this integrated solution, we modified features for context extraction described in Section 5.1 since advice revealing sentences are unknown when the extraction algorithm is executed at the first time.…”

Section: Using Multiple Linear Crfmentioning

confidence: 99%

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Toward advice mining

Myaeng

2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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Web forums are platforms for personal communications on sharing information with others. Such information is often expressed in the form of advice. In this paper, we address the problem of advice-revealing text unit (ATU) extraction from online forums due to its usefulness in travel domain. We represent advice as a two-tuple comprising an advicerevealing sentence and its context sentences. To extract the advice-revealing sentences, we propose to define the task as a sequence labeling problem, using three different types of features: syntactic, contextual, and semantic features. To extract the context sentences, we propose to use a 2 Dimensional CRF (2D-CRF) model, which gives the best performance compared to traditional machine learning models. Finally, we present a solution to the integrated problem of extracting both advice-revealing sentences and their respective context sentences at the same time using our proposed models, i.e., Multiple Linear CRF (ML-CRF) and 2 Dimensional CRF Plus (2D-CRF+). The experimental results show that ML-CRF performs better than any other models studied in this paper for extracting advice-revealing sentences and context sentences.

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Section: Using Multiple Linear Crfmentioning

confidence: 99%

“…This similarity measure is used to bridge the lexical gap between an advice-revealing sentence and its context sentence candidates. We use a sentence relatedness measure provided in Open Roget's project [10]. Feature-3.…”

Section: Extracting Context Sentences Individuallymentioning

confidence: 99%

Toward advice mining

Myaeng

2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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“…• apply the supervised measures of semantic relatedness from (Kennedy and Szpakowicz 2011) and (Kennedy and Szpakowicz 2012) to the updating of Roget's Thesaurus, and evaluate it carefully; • propose and compare three methods of automatically adding words to Roget's Thesaurus; • build the updated editions of the 1911 and 1987 versions of Roget's Thesaurus; • create new datasets for pseudo-word-sense disambiguation and the selection of the best synonym;…”

Section: Introductionmentioning

confidence: 99%

Evaluation of automatic updates of Roget’s Thesaurus

Kennedy

Śzpakowicz

2014

JLM

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Thesauri and similarly organised resources attract increasing interest of Natural Language Processing researchers. Thesauri age fast, so there is a constant need to update their vocabulary. Since a manual update cycle takes considerable time, automated methods are required. This work presents a tuneable method of measuring semantic relatedness, trained on Roget's Thesaurus, which generates lists of terms related to words not yet in the Thesaurus. Using these lists of terms, we experiment with three methods of adding words to the Thesaurus. We add, with high confidence, over 5500 and 9600 new word senses to versions of Roget's Thesaurus from 1911 and 1987 respectively. We evaluate our work both manually and by applying the updated thesauri in three NLP tasks: selection of the best synonym from a set of candidates, pseudo-word-sense disambiguation and SAT-style analogy problems. We find that the newly added words are of high quality. The additions significantly improve the performance of Roget's-based methods in these NLP tasks. The performance of our system compares favourably with that of WordNet-based methods. Our methods are general enough to work with different versions of Roget's Thesaurus.

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Supervised Distributional Semantic Relatedness

Kennedy

Śzpakowicz

2012

Text, Speech and Dialogue

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A Supervised Method of Feature Weighting for Measuring Semantic Relatedness

Cited by 3 publications

References 13 publications

Toward advice mining

Toward advice mining

Evaluation of automatic updates of Roget’s Thesaurus

Supervised Distributional Semantic Relatedness

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