EVE: explainable vector based embedding technique using Wikipedia
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|Title:||EVE: explainable vector based embedding technique using Wikipedia||Authors:||Qureshi, M. Atif
|Permanent link:||http://hdl.handle.net/10197/10460||Date:||4-Jun-2018||Online since:||2019-05-15T09:26:18Z||Abstract:||We present an unsupervised explainable vector embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a concept using human readable labels, thereby it is readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed model, we consider its usefulness in three fundamental tasks: 1) intruder detection to evaluate its ability to identify a non-coherent vector from a list of coherent vectors, 2) ability to cluster to evaluate its tendency to group related vectors together while keeping unrelated vectors in separate clusters, and 3) sorting relevant items first to evaluate its ability to rank vectors (items) relevant to the query in the top order of the result. For each task, we also propose a strategy to generate a task-specific human-interpretable explanation from the model. These demonstrate the overall effectiveness of the explainable embeddings generated by EVE. Finally, we compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across the three tasks, and report improvements over the state-of-the-art.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Springer||Journal:||Journal of Intelligent Information Systems||Start page:||1||End page:||29||Copyright (published version):||2018 Springer||Keywords:||Distributional semantics; Unsupervised learning; Wikipedia||DOI:||10.1007/s10844-018-0511-x||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Insight Research Collection|
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