Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for non-experts to annotate. We analyze 200 pairs of similar sentences and identify several underlying properties of sentence intersection. We leverage these insights to design an algorithm that decomposes the sentence intersection task into several simpler annotation tasks, facilitating the construction of a high quality dataset via crowdsourcing. We implement this approach and provide an annotated dataset of 1,764 sentence intersections.
|Original language||American English|
|Title of host publication||COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016|
|Subtitle of host publication||Technical Papers|
|Publisher||Association for Computational Linguistics, ACL Anthology|
|Number of pages||11|
|State||Published - 2016|
|Event||26th International Conference on Computational Linguistics, COLING 2016 - Osaka, Japan|
Duration: 11 Dec 2016 → 16 Dec 2016
|Name||COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers|
|Conference||26th International Conference on Computational Linguistics, COLING 2016|
|Period||11/12/16 → 16/12/16|
Bibliographical noteFunding Information:
This work was supported by the German Research Foundation via the German-Israeli Project Cooperation (grant DA 1600/1-1), the Israel Science Foundation grant 880/12, and by grants from the MAGNET program of the Israeli Office of the Chief Scientist (OCS).
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