Eytan Adar


2017

pdf bib
Learning Word Relatedness over Time
Guy D. Rosin | Eytan Adar | Kira Radinsky
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Search systems are often focused on providing relevant results for the “now”, assuming both corpora and user needs that focus on the present. However, many corpora today reflect significant longitudinal collections ranging from 20 years of the Web to hundreds of years of digitized newspapers and books. Understanding the temporal intent of the user and retrieving the most relevant historical content has become a significant challenge. Common search features, such as query expansion, leverage the relationship between terms but cannot function well across all times when relationships vary temporally. In this work, we introduce a temporal relationship model that is extracted from longitudinal data collections. The model supports the task of identifying, given two words, when they relate to each other. We present an algorithmic framework for this task and show its application for the task of query expansion, achieving high gain.

2016

pdf bib
SimpleScience: Lexical Simplification of Scientific Terminology
Yea-Seul Kim | Jessica Hullman | Matthew Burgess | Eytan Adar
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
Prototype Synthesis for Model Laws
Matthew Burgess | Eugenia Giraudy | Eytan Adar
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

pdf bib
Building a Scientific Concept Hierarchy Database (SCHBase)
Eytan Adar | Srayan Datta
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)