Paper: Generalizing Sub-sentential Paraphrase Acquisition across Original Signal Type of Text Pairs

ACL ID D12-1066
Title Generalizing Sub-sentential Paraphrase Acquisition across Original Signal Type of Text Pairs
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

This paper describes a study on the impact of the original signal (text, speech, visual scene, event) of a text pair on the task of both man- ual and automatic sub-sentential paraphrase acquisition. A corpus of 2,500 annotated sen- tences in English and French is described, and performance on this corpus is reported for an efficient system combination exploiting a large set of features for paraphrase recogni- tion. A detailed quantified typology of sub- sentential paraphrases found in our corpus types is given.