Papers for Relevance Assessment by Maayan Geffet

Research Question: We aim to improve the Distributional similarity model to better approximate the applied lexical entailment relationship between words.

Paper ID
(Link to PDF)

Title

Author(s)

H91-1077

A PROPOSAL FOR LEXICAL DISAMBIGUATION

George A. Miller; Daniel A. Teibel

W03-1809

A Statistical Approach to the Semantics of Verb-Particles

Colin Bannard; Timothy Baldwin; Alex Lascarides

C00-1050

Kana-Kanji Conversion System with Input Support Based on Prediction

Yumi ICHIMURA; Yoshimi SAITO; Kazuhiro KIMURA; Hideki HIRAKAWA

W03-0906

Entailment, intensionality and text understanding

Cleo Condoravdi; Dick Crouch; Valeria de Paiva; Reinhard Stolle; Daniel G. Bobrow

P99-1014

Inducing a Semantically Annotated Lexicon via EM-Based Clustering

Mats Rooth Stefan; Riezler Detlef Prescher

36-153

Feature Vector Quality and Distributional Similarity

Maayan Geffet and Ido Dagan

W01-1008

Document Fusion for Comprehensive Event Description

Monz, Christof

W05-1203

Measuring the Semantic Similarity of Texts

Courtney Corley; Rada Mihalcea

H93-1054

SEMANTIC CLASSES AND SYNTACTIC AMBIGUITY

Philip Resnik

16

Named Entity Recognition in Biomedical Texts using an HMM Model

Shaojun Zhao

W05-1202

The Distributional Similarity of Sub-Parses

Julie Weeds; David Weir; Bill Keller

I05-5002

Automatically Constructing a Corpus of Sentential Paraphrases

William B. Dolan; Chris Brockett

W05-1009

Morphology vs. Syntax in Adjective Class Acquisition

Gemma Boleda; Toni Badia; Sabine Schulte im Walde

I05-5003

Using Machine Translation Evaluation Techniques to Determine Sentence-level Semantic Equivalence

Andrew Finch; Young-Sook Hwang; Eiichiro Sumita

C00-2101

Learning Semantic-Level Information Extraction Rules by Type-Oriented ILP

Yutaka Sasaki; Yoshihiro Matsuo