Papers for Relevance Assessment by Patrick Sturt

Research Question: Can the parallelism preference contribute towards improving parsing accuracy?

Paper ID
(Link to PDF)

Title

Author(s)

P01-1010

What is the Minimal Set of Fragments that Achieves Maximal Parse Accuracy?

Rens Bod

H92-1041

Feature Selection and Feature Extract ion for Text Categorization

David D. Lewis

C92-1039

SYNTACTIC PREFERENCES FOR ROBUST PARSING WITH SEMANTIC PREFERENCES

JIN WANG

J93-1001

Introduction to the Special Issue on Computational Linguistics Using Large Corpora

Kenneth W. Church; Robert L. Mercert

E85-1013

Right Attachment and Preference Semantics .

Yorick Wilks

H01-1061

Robust Knowledge Discovery from Parallel Speech and Text Sources

F. Jelinek; W. Byrne; S. Khudanpur; B. Hladká; H. Ney; F.J. Och; J. Curín; J. Psutka

P98-1003

Towards a Single Proposal in Spelling Correction

Eneko Agirre; Koldo Gojenola; Kepa Sarasola; Atro Voutilainen

W91-0114

SHARED PREFERENCES

James Barnett; Inderjeet Mani

E91-1049

A PREFERENCE MECHANISM BASED ON MULTIPLE CRITERIA RESOLUTION

Yannis Dologlou; Giovanni Malnati; Patrizia Paggio

C02-1159

Extending a Broad-Coverage Parser for a General NLP Toolkit

Hassan Alam; Hua Cheng; Rachmat Hartono; Aman Kumar; Paul Llido; Crystal Nakatsu; Fuad Rahman; Yuliya Tarnikova; Timotius Tjahjadi; Che Wilcox

W03-0401

A model of syntactic disambiguation based on lexicalized grammars

Yusuke Miyao; Jun'ichi Tsujii

E87-1023

A MODEL FOR PREFERENCE

Dominique Petitpierre; Steven Krauwer; Louis des Tombe; Doug Arnold; Giovanni B. Varile

40-766

A Deterministic Word Dependency Analyzer Enhanced With Preference Learning

Hideki Isozaki, Hideto Kazawa and Tsutomu Hirao

W96-0112

A Probabilistic Disambiguation Method Based on Psycholinguistic Principles

Hang Li

185_pdf_2-col

Multi-Criteria-based Active Learning for Named Entity Recognition

Dan Shen, Jie Zhang, Jian Su, Guodong Zhou and Chew-Lim Tan