Voting Between Multiple Data Representations for Text Chunking

This paper considers the hypothesis that voting between multiple data representations can be more accurate than voting between multiple learning models This hypothesis has been considered before (cf [San00]) but the focus was on voting methods rather than the data representations In this paper, we focus on choosing specific data representations combined with simple majority voting On the community standard CoNLL-2000 data set, using no additional knowledge sources apart from the training data, we achieved 94.01 Fβ=1 score for arbitrary phrase identification compared to the previous best Fβ=1 93.90 We also obtained 95.23 Fβ=1 score for Base NP identification Significance tests show that our Base NP identification score is significantly better than the previous comparable best Fβ=1 score of 94.22 Our main contribution is that our model is a fast linear time approach and the previous best approach is significantly slower than our system.

[1]  Beáta Megyesi,et al.  Shallow Parsing with PoS Taggers and Linguistic Features , 2002, J. Mach. Learn. Res..

[2]  Hans van Halteren,et al.  Improving Data Driven Wordclass Tagging by System Combination , 1998, ACL.

[3]  Sabine Buchholz,et al.  Introduction to the CoNLL-2000 Shared Task Chunking , 2000, CoNLL/LLL.

[4]  Erik F. Tjong Kim Sang,et al.  Text Chunking by System Combination , 2000, CoNLL/LLL.

[5]  Tong Zhang,et al.  Text Chunking based on a Generalization of Winnow , 2002, J. Mach. Learn. Res..

[6]  Hae-Chang Rim,et al.  HMM Specialization with Selective Lexicalization , 1999, EMNLP.

[7]  Hae-Chang Rim,et al.  Lexicalized Hidden Markov Models for Part-of-Speech Tagging , 2000, COLING.

[8]  Rob Koeling.  Chunking with Maximum Entropy Models , 2000, CoNLL/LLL.

[9]  Erik F. Tjong Kim Sang,et al.  Memory-Based Shallow Parsing , 2002, J. Mach. Learn. Res..

[10]  Masaki Murata,et al.  Named Entity Extraction Based on A Maximum Entropy Model and Transformation Rules , 2000, ACL.

[11]  N. Fakotakis,et al.  Memory-Based Text Chunking , 1999 .

[12]  Hervé Déjean.  Learning Syntactic Structures with XML , 2000, CoNLL/LLL.

[13]  Thorsten Brants,et al.  TnT – A Statistical Part-of-Speech Tagger , 2000, ANLP.

[14]  Walter Daelemans,et al.  Complex answers: a case study using a WWW question answering system , 2001, Natural Language Engineering.

[15]  Thomas Hofmann,et al.  Hidden Markov Support Vector Machines , 2003, ICML.

[16]  Antal van den Bosch,et al.  Single-Classifier Memory-Based Phrase Chunking , 2000, CoNLL/LLL.

[17]  Christer Johansson.  A Context Sensitive Maximum Likelihood Approach to Chunking , 2000, CoNLL/LLL.

[18]  Walter Daelemans,et al.  Introduction to Special Issue on Machine Learning Approaches to Shallow Parsing , 2002, J. Mach. Learn. Res..

[19]  Shlomo Argamon,et al.  A Memory-Based Approach to Learning Shallow Natural Language Patterns , 1998, ACL.

[20]  Claire Cardie,et al.  Error-Driven Pruning of Treebank Grammars for Base Noun Phrase Identification , 1998, ACL.

[21]  Mitchell P. Marcus,et al.  Text Chunking using Transformation-Based Learning , 1995, VLC@ACL.

[22]  Beata Megyesi.  Phrasal Parsing by Using Data-Driven PoS Taggers , 2001 .

[23]  Ferran Plà,et al.  Improving Chunking by Means of Lexical-Contextual Information in Statistical Language Models , 2000, CoNLL/LLL.

[24]  Hans van Halteren.  Chunking with WPDV Models , 2000, CoNLL/LLL.

[25]  Srinivas Bangalore,et al.  Performance Evaluation of Supertagging for Partial Parsing , 2000 .

[26]  Erik F. Tjong Kim Sang,et al.  Representing Text Chunks , 1999, EACL.

[27]  Walter Daelemans,et al.  Improving Data Driven Wordclass Tagging by System Combination , 2022, International Conference on Computational Linguistics.

[28]  David S. Day,et al.  Phrase Parsing with Rule Sequence Processors: an Application to the Shared CoNLL Task , 2000, CoNLL/LLL.

[29]  Anne Abeillé,et al.  A Lexicalized Tree Adjoining Grammar for English , 1990 .

[30]  Walter Daelemans,et al.  Applying System Combination to Base Noun Phrase Identification , 2000, COLING.

[31]  Park,et al.  Identifying the Interaction between Genes and Gene Products Based on Frequently Seen Verbs in Medline Abstracts. , 1998, Genome informatics. Workshop on Genome Informatics.

[32]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[33]  Kenneth Ward Church.  A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text , 1988, ANLP.

[34]  Steven Abney,et al.  Parsing By Chunks , 1991 .

[35]  Eugene Charniak,et al.  Statistical language learning , 1997 .

[36]  Shlomo Argamon,et al.  A Memory-Based Approach to Learning Shallow Natural Language Patterns , 1999, COLING.

[37]  Michael Collins,et al.  Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms , 2002, EMNLP.

[38]  Jian Su,et al.  Hybrid Text Chunking , 2000, CoNLL/LLL.

[39]  Wei Li,et al.  Information Extraction Supported Question Answering , 1999, TREC.

[40]  Miles Osborne,et al.  Shallow Parsing as Part-of-Speech Tagging , 2000, CoNLL/LLL.

[41]  Bernard Mérialdo,et al.  Tagging English Text with a Probabilistic Model , 1994, CL.

[42]  Dan Roth,et al.  A Learning Approach to Shallow Parsing , 1999, EMNLP.

[43]  Beatrice Santorini,et al.  Part-of-Speech Tagging Guidelines for the Penn Treebank Project (3rd Revision) , 1990 .

[44]  Stephen Cox,et al.  Some statistical issues in the comparison of speech recognition algorithms , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[45]  Walter Daelemans,et al.  Cascaded Grammatical Relation Assignment , 1999, EMNLP.

[46]  Ferran Plà,et al.  Shallow Parsing using Specialized HMMs , 2002, J. Mach. Learn. Res..

[47]  Wolfgang Wahlster,et al.  Verbmobil: Foundations of Speech-to-Speech Translation , 2000, Artificial Intelligence.

[48]  Fernando Pereira,et al.  Shallow Parsing with Conditional Random Fields , 2003, NAACL.

[49]  Ann Bies,et al.  Bracketing Guidelines For Treebank II Style Penn Treebank Project , 1995 .

[50]  Nianwen Xue,et al.  Chinese Word Segmentation as LMR Tagging , 2003, SIGHAN.

[51]  Thorsten Brants,et al.  Cascaded Markov Models , 1999, EACL.

[52]  Yuji Matsumoto,et al.  Chunking with Support Vector Machines , 2001, NAACL.

[53]  Aravind K. Joshi,et al.  34th Annual Meeting of the Association for Computational Linguistics , 1996 .

[54]  XTAG Research Group,et al.  A Lexicalized Tree Adjoining Grammar for English , 1998, ArXiv.

[55]  Wojciech Skut,et al.  Chunk Tagger - Statistical Recognition of Noun Phrases , 1998, ArXiv.