Text classification with the support of pruned dependency patterns

Özgür, Levent; Güngör, Tunga

doi:10.1016/j.patrec.2010.05.005

Cited by 21 publications

(2 citation statements)

References 13 publications

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“…In terms of the specific task tackled in this article, there could be more investigation into how precisely this author and distance information are causing the improvement discovered, and whether more sophisticated models could capture this. In terms of alternative approaches, dependency relations could be promising, either in terms of structured language models (Xu, Chelba and Jelinek 2002) or as additional features in the supervised model (Özgür and Güngör 2010). In terms of applications, the techniques in the article could be applied to the sentiment-directed text rewriting noted at the start of the article described in Inkpen et al 2006, or the intelligent thesaurus of Inkpen (2007a).…”

Section: Resultsmentioning

confidence: 99%

Predicting word choice in affective text

Gardiner

Dras

2015

Nat. Lang. Eng.

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Choosing the best word or phrase for a given context from among the candidate nearsynonyms, such as slim and skinny, is a difficult language generation problem. In this paper, we describe approaches to solving an instance of this problem, the lexical gap problem, with a particular focus on affect and subjectivity; to do this we draw upon techniques from the sentiment and subjectivity analysis fields. We present a supervised approach to this problem, initially with a unigram model that solidly outperforms the baseline, with a 6.8% increase in accuracy. The results to some extent confirm those from related problems, where feature presence outperforms feature frequency, and immediate context features generally outperform wider context features. However, this latter is somewhat surprisingly not always the case, and not necessarily where intuition might first suggest; and an analysis of where document-level models are in some cases better suggested that, in our corpus, broader features related to the 'tone' of the document could be useful, including document sentiment, document author, and a distance metric for weighting the wider lexical context of the gap itself. From these, our best model has a 10.1% increase in accuracy, corresponding to a 38% reduction in errors. Moreover, our models do not just improve accuracy on affective word choice, but on non-affective word choice also.

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Section: Resultsmentioning

confidence: 99%

Predicting word choice in affective text

Gardiner

Dras

2015

Nat. Lang. Eng.

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“…Similarly, two approaches on keyword extraction for a cluster/class are corpus-based and class-based keyword selection [12,21]. The corpus-based keyword selection is applied in classification problems by filtering the low frequency features that appear, in the corpus, less than a threshold value [27]. On the other hand, the class-based keyword selection identifies important keywords (features) for each class with the class-based metric, such as ICF and mutual information, via comparison of statistics among clusters or classes.…”

Section: Related Workmentioning

confidence: 99%

Effect of Term Weighting on Keyword Extraction in Hierarchical Category Structure

Chiraratanasopha

Boonbrahm

Theeramunkong³

2021

cai

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While there have been several studies related to the effect of term weighting on classification accuracy, relatively few works have been conducted on how term weighting affects the quality of keywords extracted for characterizing a document or a category (i.e., document collection). Moreover, many tasks require more complicated category structure, such as hierarchical and network category structure, rather than a flat category structure. This paper presents a qualitative and quantitative study on how term weighting affects keyword extraction in the hierarchical category structure, in comparison to the flat category structure. A hierarchical structure triggers special characteristic in assigning a set of keywords or tags to represent a document or a document collection, with support of statistics in a hierarchy, including category itself, its parent category, its child categories, and sibling categories. An enhancement of term weighting is proposed particularly in the form of a series of modified TFIDF's, for improving keyword extraction. A text collection of public-hearing opinions is used to evaluate variant TFs and IDFs to identify which types of information in hierarchical category structure are useful. By experiments, B. Chiraratanasopha, S. Boonbrahm, T. Theeramunkong we found that the most effective IDF family, namely TF-IDFr, is identity > sibling > child > parent in order. The TF-IDFr outperforms the vanilla version of TFIDF with a centroid-based classifier.

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