Papers for Relevance Assessment by Abhishek Arun

Research Question: Do standard probabilistic parsing techniques, developed for English, fare well for French and does lexicalistion help improve parsing results ?

Reformulation: Do standard probabilistic parsing techniques, developed for English, fare well for French and does lexicalization help improve parsing results ?

Paper ID
(Link to PDF)

Title

Author(s)

W97-0202

Experience in WordNet Sense Tagging in the Wall Street Journal

Janyce Wiebel; Julie Maples; Lei Duanl; Rebecca Bruce

P05-1039

What to Do When Lexicalization Fails: Parsing German with Suffix Analysis and Smoothing

Amit Dubey

W99-0622

Guiding a Well-Founded Parser with Corpus Statistics

Amon Seagull; Lenhart Schubert

H91-1070

Interactive Problem Solving and Dialogue in the ATIS Domain

Stephanie Seneff; Lynette Hirschman; Victor W. Zue

J99-2004

Supertagging: An Approach to Almost Parsing

Srinivas Bangalore; Aravind K. Joshi

H90-1022

Developing an Evaluation Methodology for Spoken Language Systems

Madeleine Bates; Sean Boisen; John Makhoul

C00-1081

A Stochastic Parser Based on a Structural Word Prediction Model

Shinsuke MORI; Masafumi NISHIMURA; Nobuyasu ITOH; Shiho OGINO; Hideo WATANABE

P00-1061

Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training

Stefan Riezler; Detlef Prescher; Jonas Kuhn; Mark Johnson

Bikel

A Distributional Analysis of a Lexicalized Statistical Parsing Model

Daniel M. Bikel

W01-0707

Probabilistic models for PP-attachment resolution and NP analysis

Gaussier, Eric; Cancedda, Nicola

W98-1114

Can Subcategorisation Probabilities Help a Statistical Parser

John Carroll; Guido Minnen; Ted Briscoe

P96-1025

A New Statistical Parser Based on Bigram Lexical Dependencies

Michael John Collins

W03-0303

Word Alignment Based on Bilingual Bracketing

Bing Zhao; Stephan Vogel

W05-1512

Head-Driven PCFGs with Latent-Head Statistics

Detlef Prescher

W00-0718

ALLiS: a Symbolic Learning System for Natural Language Learning

Herve Dejean