Papers for Relevance Assessment by David Schlangen

Research Question: Can Fragments, a certain class of non-sentential utterances, be automatically detected and linked up with their antecedents, and can criteria for this task be learned using machine learning techniques?

Paper ID
(Link to PDF)

Title

Author(s)

P96-1057

Processing Complex Sentences in the Centering Framework

Michael Strube

W94-0110

Combining Linguistic with Statistical Methods in Automatic Speech Understanding

Patti Price

C94-1098

A PARSER COPING WITH SELF-REPAIRED JAPANESE UTTERANCES AND LARGE CORPUS-BASED EVALUATION

Yuji Sagawa; Noboru Ohnishi; Noboru Sugie

P98-1048

Experiments with Learning Parsing Heuristics

Sylvain Delisle; Sylvain Letourneau; Stan Marwin

W05-1519

Exploring Features for Identifying Edited Regions in Disfluent Sentences

Qi Zhang; Fuliang Weng

P93-1007

A SPEECH-FIRST MODEL FOR REPAIR DETECTION AND CORRECTION

Christine Nakatani; Julia Hirschberg

N03-3007

Word Fragments Identification Using Acoustic-Prosodic Features in Conversational Speech

Yang Liu

E03-3001

Learning to Identify Fragmented Words in Spoken Discourse

Piroska Lendvai

P92-1008

INTEGRATING MULTIPLE KNOWLEDGE SOURCES FOR DETECTION AND CORRECTION OF REPAIRS IN HUMAN-COMPUTER DIALOG

John Bear; John Dowding; Elizabeth Shribergf

C90-2017

Discourse Anaphora

Joke Dorrepaal

I05-4001

Domain Knowledge Engineering Based on Encyclopedias and the Web Text

Sui Zhifang; Cui Gaoying; Ding Wansong; Zhang Qinlong

W97-1515

Experiences with the GTU grammar development environment

Martin Volk

W98-1418

GENERATION OF NOUN COMPOUNDS IN HEBREW: CAN SYNTACTIC KNOWLEDGE BE FULLY ENCAPSULATED?

Yael Dahan Netzer; Michael Elhadad

11

Word Order Variation in German Main Clauses. A Corpus Analysis

Andrea Weber and Karin Müller

H93-1066

A SPEECH-FIRST MODEL FOR REPAIR DETECTION AND CORRECTION

Christine Nakatani; Julia Hirschberg