Dialogue-Act Tagging Using Smart Feature Selection; Results on Multiple Corpora

Verbree, Daan; Rienks, Rutger; Heylen, Dirk

doi:10.1109/slt.2006.326819

Cited by 32 publications

(39 citation statements)

References 13 publications

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“…Large numbers of n-grams are generated by any real-world corpus so a predictive subset is used (in [3] sets of 10 or 300). N-grams have been used to code features for Hidden Markov Models [4].…”

Section: Features For Da Classificationmentioning

confidence: 99%

“…Dialogue Act (DA) classification is an established element of research in the field of Dialogue Management [1][2][3][4][5][6]. This work is motivated by the application of DA classification to natural language interaction with Dialogue Systems (DSs) [7] and Robots [8].…”

Section: Introductionmentioning

confidence: 99%

“…Machine classification of DAs has used various techniques including n-gram models [2], Bayesian networks [10] and Decision Trees (DTs) [3]. These approaches have achieved good results, but they use computationally intensive feature extraction which rules them out from future realtime applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Machine Learning Approach to Speech Act Classification Using Function Words

O’Shea

Bandar

Crockett

2010

Agent and Multi-Agent Systems: Technologies and Applications

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Abstract. This paper presents a novel technique for the classification of sentences as Dialogue Acts, based on structural information contained in function words. It focuses on classifying questions or non-questions as a generally useful task in agent-based systems. The proposed technique extracts salient features by replacing function words with numeric tokens and replacing each content word with a standard numeric wildcard token. The Decision Tree, which is a well-established classification technique, has been chosen for this work. Experiments provide evidence of potential for highly effective classification, with a significant achievement on a challenging dataset, before any optimisation of feature extraction has taken place.

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Section: Features For Da Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Approach to Speech Act Classification Using Function Words

O’Shea

Bandar

Crockett

2010

Agent and Multi-Agent Systems: Technologies and Applications

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“…Some syntactic relations are captured by HMM word models, such as the widely-used n-grams [11], but these approaches only capture local syntactic relations, while we consider next global syntactic trees. Most other works thus focus on morphosyntactic tags, as demonstrated for instance in [48], where a smart compression technique for feature selection is introduced. The authors use a rich feature set with POS-tags included and obtain with a decision tree classifier an accuracy of 89.27%, 65.68% and 59.76% respectively on the ICSI, Switchboard and on a selection of the AMI corpus.…”

Section: Related Workmentioning

confidence: 99%

Automatic dialogue act recognition with syntactic features

Král

Cerisara

2014

Lang Resources & Evaluation

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This work studies the usefulness of syntactic information in the context of automatic dialogue act recognition in Czech. Several pieces of evidence are presented in this work that support our claim that syntax might bring valuable information for dialogue act recognition. In particular, a parallel is drawn with the related domain of automatic punctuation generation and a set of syntactic features derived from a deep parse tree is further proposed and successfully used in a Czech dialogue act recognition system based on Conditional Random Fields. We finally discuss the possible reasons why so few works have exploited this type of information before and propose future research directions to further progress in this area.

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“…As the literature indicates that only the ranking approaches have been investigated (Samuel et al, 1999;Webb et al, 2205;Lesch, 2005;Kats, 2006;Verbree et al, 2006) due to their computational efficiency, regardless of their inefficiency with respect to the relevance and redundancy of the selection. In addition to examining the proposed VLGA on the lexical cue selection, a number of ranking approaches which are always applied for cue phrase selection have been experimented.…”

Section: C)p(f C) P(f C)p(f C)] X(f C) P(f )P(f )P(c)p(cmentioning

confidence: 99%

Feature Selection for High Dimensional Data: An Evolutionary Filter Approach

Yahya¹,

Osman²,

Ramli³

et al. 2011

Journal of Computer Science

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Problem statement: Feature selection is a task of crucial importance for the application of machine learning in various domains. In addition, the recent increase of data dimensionality poses a severe challenge to many existing feature selection approaches with respect to efficiency and effectiveness. As an example, genetic algorithm is an effective search algorithm that lends itself directly to feature selection; however this direct application is hindered by the recent increase of data dimensionality. Therefore adapting genetic algorithm to cope with the high dimensionality of the data becomes increasingly appealing. Approach: In this study, we proposed an adapted version of genetic algorithm that can be applied for feature selection in high dimensional data. The proposed approach is based essentially on a variable length representation scheme and a set of modified and proposed genetic operators. To assess the effectiveness of the proposed approach, we applied it for cues phrase selection and compared its performance with a number of ranking approaches which are always applied for this task. Results and Conclusion: The results provide experimental evidences on the effectiveness of the proposed approach for feature selection in high dimensional data.

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Dialogue-Act Tagging Using Smart Feature Selection; Results on Multiple Corpora

Cited by 32 publications

References 13 publications

A Machine Learning Approach to Speech Act Classification Using Function Words

A Machine Learning Approach to Speech Act Classification Using Function Words

Automatic dialogue act recognition with syntactic features

Feature Selection for High Dimensional Data: An Evolutionary Filter Approach

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