{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:37:03Z","timestamp":1723016223863},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Representing a sentence with a fixed vector has shown its effectiveness in various NLP tasks. Most of the existing methods are based on neural network, which recursively apply different composition functions to a sequence of word vectors thereby obtaining a sentence vector.A hypothesis behind these approaches is that the meaning of any phrase can be composed of the meanings of its constituents.However, many phrases, such as idioms, are apparently non-compositional.To address this problem, we introduce a parameterized compositional switch, which outputs a scalar to adaptively determine whether the meaning of a phrase should be composed of its two constituents.We evaluate our model on five datasets of sentiment classification and demonstrate its efficacy with qualitative and quantitative experimental analysis .<\/jats:p>","DOI":"10.24963\/ijcai.2017\/567","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"4061-4067","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Semantic Compositionality for Sentence Modelling"],"prefix":"10.24963","author":[{"given":"Pengfei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"},{"name":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xipeng","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"},{"name":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanjing","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"},{"name":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:54:33Z","timestamp":1501228473000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/567"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/567","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}