Genetic algorithms for feature relevance assignment in memory-based language processing

Kool, Anne; Daelemans, Walter; Zavrel, Jakub

doi:10.3115/1117601.1117624

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…An inductive method with GA for learning phrase-structure-rule of natural language has been proposed by Wang and Dai (1996). Among other potential application areas, the use of GA can be found in information retrieval (Losee 2000), morphology (Kazakov 1997), dialogue systems (Blasband 1998), grammar inference (Lankhorst 1994) and memory-based language processing (Kool et al 2000).…”

Section: Use Of Ga For Solving Optimization Problemsmentioning

confidence: 99%

Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition

Ekbal

Saha

2010

Res on Lang and Comput

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In this paper, we propose a classifier ensemble technique based on genetic algorithm (GA) for named entity recognition (NER). We assume that the classifiers based on different feature representations can be effectively combined together using GA to achieve better performance. The proposed approach is also able to find the appropriate ensemble approach, i.e. either majority voting or weighted voting. Maximum entropy (ME) model is used as a base to generate a number of different classifiers depending upon the various representations of the available features. The proposed approach is evaluated for three leading Indian languages, namely Bengali, Hindi and Telugu. Evaluation results yield the recall, precision and F-measure values of 88.12, 93.99 and 90.96%, respectively for Bengali, 80.26, 92.70 and 86.03%, respectively for Hindi and 74.79, 85.38 and 79.73%, respectively for Telugu. We also evaluate the proposed approach with the CoNLL-2003 benchmark English datasets and it shows the recall, precision and F-measure values of 83.05, 85.52 and 84.27%, respectively. It is observed that the GA based ensemble attains the performance which is superior to all the individual classifiers as well as two conventional baseline ensembles for all the languages.

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Section: Use Of Ga For Solving Optimization Problemsmentioning

confidence: 99%

Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition

Ekbal

Saha

2010

Res on Lang and Comput

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“…De Pauw et al (2004) built on this work and compared a memory-based learning method with a finite state method. One of the characteristic features of Dutch is diminutive formation (Trommelen, 1983) and computational approaches have been explored to predict the correct diminutive suffix in Dutch (Daelemans et al, 1996;Kool et al, 2000).…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Intra-Word Code-Switching

Nguyen¹,

Cornips²

2016

Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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Welcome to the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology. The workshop aims to bring together researchers interested in applying computational techniques to problems in morphology, phonology, and phonetics. Our program this year highlights the ongoing and important interaction between work in computational linguistics and work in theoretical linguistics. We received 23 submissions and accepted 11.The volume of submissions made it necessary to recruit several additional reviewers. We'd like to thank all of these people for agreeing to review papers on what seemed like impossibly short notice.This year also marks the first SIGMORPHON shared task, on morphological reinflection. The shared task received 9 submissions, all of which were accepted, and greatly advanced the state of the art in this area.We thank all the authors, reviewers and organizers for their efforts on behalf of the community. AbstractThis paper conceptualizes speech prosody data mining and its potential application in data-driven phonology/phonetics research. We first conceptualize Speech Prosody Mining (SPM) in a time-series data mining framework. Specifically, we propose using efficient symbolic representations for speech prosody time-series similarity computation. We experiment with both symbolic and numeric representations and distance measures in a series of time-series classification and clustering experiments on a dataset of Mandarin tones. Evaluation results show that symbolic representation performs comparably with other representations at a reduced cost, which enables us to efficiently mine large speech prosody corpora while opening up to possibilities of using a wide range of algorithms that require discrete valued data. We discuss the potential of SPM using time-series mining techniques in future works. IntroductionCurrent investigations on the phonology of intonation and tones (or pitch accent) typically employ data-driven approaches by building research on top of manual annotations of a large amount of speech prosody data (for example, (Morén and Zsiga, 2006; Zsiga and Zec, 2013), and many others). Meanwhile, researchers are also limited by the amount of resources invested in such expensive endeavor of manual annotations. Given this paradox, we believe that this type of data driven approach in phonology-phonetics interface can benefit from tools that can efficiently index, query, classify, cluster, summarize, and discover meaningful prosodic patterns from a large speech prosody corpus.The data mining of f 0 1 (pitch) contour patterns from audio data has recently gained success in the domain of Music Information Retrieval (aka MIR, see (Gulati and Serra, 2014; Gulati et al., 2015; Ganguli, 2015) for examples). In contrast, the data mining of speech prosody f 0 data (here on referred to as Speech Prosody Mining (SPM) 2 ) is a less explored research topic (Raskinis and Kazlauskiene, 2013). Fundamentally, SPM in a large prosody corpus aims at discovering meaningful patterns in the f ...

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“…In natural language processing more generally, optimization problems have been studied by Kool, Zavrel and Daelemans (2000) and Daelemans et al . (2003), who use genetic algorithms for model selection in the context of part-of-speech tagging, grapheme-to-phoneme conversion with stress assignment, and word sense disambiguation.…”

Section: Related Workmentioning

confidence: 99%

MaltOptimizer: Fast and effective parser optimization

Ballesteros

Nivre

2014

Nat. Lang. Eng.

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Statistical parsers often require careful parameter tuning and feature selection. This is a nontrivial task for application developers who are not interested in parsing for its own sake, and it can be time-consuming even for experienced researchers. In this paper we present MaltOptimizer, a tool developed to automatically explore parameters and features for MaltParser, a transition-based dependency parsing system that can be used to train parser's given treebank data. MaltParser provides a wide range of parameters for optimization, including nine different parsing algorithms, an expressive feature specification language that can be used to define arbitrarily rich feature models, and two machine learning libraries, each with their own parameters. MaltOptimizer is an interactive system that performs parser optimization in three stages. First, it performs an analysis of the training set in order to select a suitable starting point for optimization. Second, it selects the best parsing algorithm and tunes the parameters of this algorithm. Finally, it performs feature selection and tunes machine learning parameters. Experiments on a wide range of data sets show that MaltOptimizer quickly produces models that consistently outperform default settings and often approach the accuracy achieved through careful manual optimization.

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Genetic algorithms for feature relevance assignment in memory-based language processing

Cited by 4 publications

References 15 publications

Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition

Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition

Automatic Detection of Intra-Word Code-Switching

MaltOptimizer: Fast and effective parser optimization

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