Memory-Based Shallow Parsing

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement.

[1]  Walter Daelemans,et al.  Forgetting Exceptions is Harmful in Language Learning , 1998, Machine Learning.

[2]  Michael Collins,et al.  Head-Driven Statistical Models for Natural Language Parsing , 2003, CL.

[3]  Booncharoen Sirinaovakul,et al.  Introduction to the Special Issue , 2002, Comput. Intell..

[4]  Steven Abney,et al.  Statistical Methods and Linguistics , 2002 .

[5]  Xavier Carreras,et al.  Boosting trees for clause splitting , 2001, CoNLL.

[6]  Ferran Plà,et al.  Clause detection using HMM , 2001, CoNLL.

[7]  Erik F. Tjong Kim Sang,et al.  Memory-based clause identification , 2001, CoNLL.

[8]  Michele Banko,et al.  Scaling to Very Very Large Corpora for Natural Language Disambiguation , 2001, ACL.

[9]  James Hammerton.  Clause identification with long short-term memory , 2001, CoNLL.

[10]  Jon D. Patrick,et al.  Boosted decision graphs for NLP learning tasks , 2001, CoNLL.

[11]  Hervé Déjean.  Using ALLiS for clausing , 2001, CoNLL.

[12]  Tong Zhang,et al.  Text Chunking using Regularized Winnow , 2001, ACL.

[13]  Rens Bod,et al.  What is the Minimal Set of Fragments that Achieves Maximal Parse Accuracy? , 2001, ACL.

[14]  Hervé Déjean,et al.  Introduction to the CoNLL-2001 shared task: clause identification , 2001, CoNLL.

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

[16]  Walter Daelemans,et al.  Improving Accuracy in word class tagging through the Combination of Machine Learning Systems , 2001, CL.

[17]  Ido Dagan,et al.  Incorporating Compositional Evidence in Memory-Based Partial Parsing , 2000, ACL.

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

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

[20]  Yuji Matsumoto,et al.  Use of Support Vector Learning for Chunk Identification , 2000, CoNLL/LLL.

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

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

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

[24]  Rens Bod,et al.  Parsing with the Shortest Derivation , 2000, COLING.

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

[26]  Alexander S. Yeh,et al.  More accurate tests for the statistical significance of result differences , 2000, COLING.

[27]  Eugene Charniak,et al.  A Maximum-Entropy-Inspired Parser , 2000, ANLP.

[28]  Erik F. Tjong Kim Sang,et al.  Noun Phrase Recognition by System Combination , 2000, ANLP.

[29]  Erik F. Tjong Kim Sang,et al.  Transforming a Chunker to a Parser , 2000, CLIN.

[30]  Erik F. Tjong Kim Sang.  Noun Phrase Recognition by System Combination , 2000, ANLP.

[31]  Gregory Grefenstette.  Light parsing as finite state filtering , 1999 .

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

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

[34]  Walter Daelemans,et al.  Introduction to the special issue on memory-based language processing , 1999, J. Exp. Theor. Artif. Intell..

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

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

[37]  Walter Daelemans,et al.  Machine learning for modeling Dutch pronunciation variation , 1999, CLIN.

[38]  Miles Osborne,et al.  MDL-based DCG Induction for NP Identification , 1999, CoNLL.

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

[40]  Mitchell P. Marcus,et al.  Maximum entropy models for natural language ambiguity resolution , 1998 .

[41]  Jorn Veenstra Sabine Buchholz.  Fast NP Chunking Using Memory-Based Learning Techniques , 1998 .

[42]  Walter Daelemans,et al.  TiMBL: Tilburg Memory-Based Learner, version 2.0, Reference guide , 1998 .

[43]  S. Buchholz,et al.  Distinguishing complements from adjuncts using memory-based learning , 1998 .

[44]  Eugene Charniak,et al.  Statistical Parsing with a Context-Free Grammar and Word Statistics , 1997, AAAI/IAAI.

[45]  Adwait Ratnaparkhi,et al.  A Linear Observed Time Statistical Parser Based on Maximum Entropy Models , 1997, EMNLP.

[46]  Walter Daelemans,et al.  Memory-Based Learning: Using Similarity for Smoothing , 1997, ACL.

[47]  Walter Daelemans,et al.  Resolving PP attachment Ambiguities with Memory-Based Learning , 1997, CoNLL.

[48]  Judith L. Klavans,et al.  Book Reviews: The Balancing Act: Combining Symbolic and Statistical Approaches to Language , 1997, CL.

[49]  David S. Day,et al.  Finite-state phrase parsing by rule sequences , 1996, COLING.

[50]  Walter Daelemans,et al.  MBT: A Memory-Based Part of Speech Tagger-Generator , 1996, VLC@COLING.

[51]  François Yvon.  Prononcer par analogie : motivation, formalisation et evaluation , 1996 .

[52]  Adwait Ratnaparkhi,et al.  A Maximum Entropy Model for Part-Of-Speech Tagging , 1996, EMNLP.

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

[54]  David M. Magerman.  Statistical Decision-Tree Models for Parsing , 1995, ACL.

[55]  Claire Cardie,et al.  Domain-specific knowledge acquisition for conceptual sentence analysis , 1995 .

[56]  Walter Daelemans,et al.  The Acquisition of Stress: A Data-Oriented Approach , 1994, Comput. Linguistics.

[57]  Rich Caruana,et al.  Greedy Attribute Selection , 1994, ICML.

[58]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[59]  Eric Brill,et al.  Some Advances in Transformation-Based Part of Speech Tagging , 1994, AAAI.

[60]  David M. Magerman.  Natural Language Parsing as Statistical Pattern Recognition , 1994, ArXiv.

[61]  David W. Aha,et al.  Feature Selection for Case-Based Classification of Cloud Types: An Empirical Comparison , 1994 .

[62]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[63]  Walter Daelemans,et al.  Memory-based lexical acquisition and processing , 1993, EAMT.

[64]  Eric Brill,et al.  A Simple Rule-Based Part of Speech Tagger , 1992, HLT.

[65]  E. Brill.  A Simple Rule-Based Part of Speech Tagger , 1992, HLT.

[66]  Robert C. Berwick,et al.  Principle-Based Parsing: Computation and Psycholinguistics , 1991 .

[67]  Howard C. Nusbaum,et al.  Pronounce : a program for pronunciation by analogy , 1991 .

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

[69]  Casimir A. Kulikowski,et al.  Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems , 1990 .

[70]  S. T. Buckland,et al.  Computer-Intensive Methods for Testing Hypotheses. , 1990 .

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