Word2Vec’s Skip Gram model is the current state-of-the-art approach for estimating the distributed representation of words. However, it assumes a single vector per word, which is not well-suited for representing words that have multiple senses. This work presents LDMI, a new model for estimating distributional representations of words. LDMI relies on the idea that, if a word carries multiple senses, then having a different representation for each of its senses should lead to a lower loss associated with predicting its co-occurring words, as opposed to the case when a single vector representation is used for all the senses. After identifying the multi-sense words, LDMI clusters the occurrences of these words to assign a sense to each occurrence. Experiments on the contextual word similarity task show that LDMI leads to better performance than competing approaches.
|Original language||English (US)|
|Title of host publication||Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings|
|Editors||Bao Ho, Dinh Phung, Geoffrey I. Webb, Vincent S. Tseng, Mohadeseh Ganji, Lida Rashidi|
|Number of pages||13|
|State||Published - 2018|
|Event||22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia|
Duration: Jun 3 2018 → Jun 6 2018
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018|
|Period||6/3/18 → 6/6/18|
Bibliographical noteFunding Information:
Acknowledgments. This work was supported in part by NSF (IIS-1247632, IIP-1414153, IIS-1447788, IIS-1704074, CNS-1757916), Army Research Office (W911NF-14-1-0316), Intel Software and Services Group, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.
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