A second-order Hidden Markov Model for part-of-speech tagging

Thede, Scott M.; Harper, Mary P.

doi:10.3115/1034678.1034712

Cited by 96 publications

(53 citation statements)

References 16 publications

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“…Intuitively, higher order HMMs can be expected to be more accurate (Thede & Happer, 1999). However, the lack of coverage problem needs to be considered, as mentioned in Section 3.2.…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

A hybrid model for business process event and outcome prediction

Gabrys

Nauck

2014

Expert Systems

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Large service companies run complex customer service processes to provide communication services to their customers. The flawless execution of these processes is essential because customer service is an important differentiator. They must also be able to predict if processes will complete successfully or run into exceptions in order to intervene at the right time, preempt problems and maintain customer service. Business process data are sequential in nature and can be very diverse. Thus, there is a need for an efficient sequential forecasting methodology that can cope with this diversity. This paper proposes two approaches, a sequential k nearest neighbour and an extension of Markov models both with an added component based on sequence alignment. The proposed approaches exploit temporal categorical features of the data to predict the process next steps using higher order Markov models and the process outcomes using sequence alignment technique. The diversity aspect of the data is also added by considering subsets of similar process sequences based on k nearest neighbours. We have shown, via a set of experiments, that our sequential k nearest neighbour offers better results when compared with the original ones; our extension Markov model outperforms random guess, Markov models and hidden Markov models.

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“…Intuitively, higher order HMMs can be expected to be more accurate (Thede & Happer, 1999). However, the lack of coverage problem needs to be considered, as mentioned in Section 3.2.…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

A hybrid model for business process event and outcome prediction

Gabrys

Nauck

2014

Expert Systems

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“…POS tags were obtained using the tagger PoST [14], [15]. This is an HMM based tagger which performs discriminative reranking of N-best hypotheses using features derived from n-grams.…”

Section: Part-of-speech (Pos) Tag N-gramsmentioning

confidence: 99%

Any questions? Automatic question detection in meetings

Boakye

Favre

Hakkani‐Tür

2009

2009 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

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Abstract-In this paper, we describe our efforts toward the automatic detection of English questions in meetings. We analyze the utility of various features for this task, originating from three distinct classes: lexico-syntactic, turn-related, and pitchrelated. Of particular interest is the use of parse tree information in classification, an approach as yet unexplored. Results from experiments on the ICSI MRDA Corpus demonstrate that lexicosyntactic features are most useful for this task, with turn-and pitch-related features providing complementary information in combination. In addition, experiments using reference parse trees on the Broadcast Conversation portion of the OntoNotes release 2.9 data set illustrate the potential of parse trees to outperform word lexical features.

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“…It is done in two stages: first, the contextual rules defined in SintaGest are applied; then, remaining ambiguities are suppressed with a statistical POS tagger based on a second-order hidden Markov model (HMM). This turns out to be a fast and efficient approach using the Viterbi algorithm [8,9]. The prior contextual and lexical probabilities were estimated by processing large, partially tagged corpora, among them the CETEMPúblico 1.7 collection of news from the Portuguese newspaper Público 5 .…”

Section: Software Toolsmentioning

confidence: 99%

Priberam’s Question Answering System for Portuguese

Amaral¹,

Figueira²,

Martins³

et al. 2006

Accessing Multilingual Information Repositories

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This paper describes the work done by Priberam in the development of a question answering (QA) system for Portuguese. The system was built using the company's NLP workbench and information retrieval technology. Special focus is given to the question analysis, document and sentence retrieval, and answer extraction stages. The paper discusses the system's performance in the context of the QA@CLEF 2005 evaluation.

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A second-order Hidden Markov Model for part-of-speech tagging

Cited by 96 publications

References 16 publications

A hybrid model for business process event and outcome prediction

A hybrid model for business process event and outcome prediction

Any questions? Automatic question detection in meetings

Priberam’s Question Answering System for Portuguese

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