At the meeting of MCQLL this week, Kushal Arora will present his recent work with Aishik Chakraborty.
Title: Learning Lexical Subspaces in a Distributional Vector Space
Abstract: In this paper, we propose LexSub, a novel approach towards unifying lexical and distributional semantics. We inject knowledge about lexical-semantic relations into distributional word embeddings by defining subspaces of the distributional vector space in which a lexical relation should hold. Our framework can handle symmetric attract and repel relations (e.g., synonymy and antonymy, respectively), as well as asymmetric relations (e.g., hypernymy and meronomy). In a suite of intrinsic benchmarks, we show that our model outperforms previous post-hoc approaches on relatedness tasks, and on hypernymy classification and detection while being competitive on word similarity tasks. It also outperforms previous systems on extrinsic classification tasks that benefit from exploiting lexical relational cues. We perform a series of analyses to understand the behaviors of our model.
This meeting will be held in room 117 at 14:30 on Wednesday.