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Beyond Context: A New Perspective for Word Embeddings
<span title="">2019</span>
<i title="Association for Computational Linguistics">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/62lejef5rzghbafcrd6wj6pc7u" style="color: black;">Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*</a>
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Most word embeddings today are trained by optimizing a language modeling goal of scoring words in their context, modeled as a multiclass classification problem. Despite the successes of this assumption, it is incomplete: in addition to its context, orthographical or morphological aspects of words can offer clues about their meaning. In this paper, we define a new modeling framework for training word embeddings that captures this intuition. Our framework is based on the well-studied problem of
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... lti-label classification and, consequently, exposes several design choices for featurizing words and contexts, loss functions for training and score normalization. Indeed, standard models such as CBOW and FAST-TEXT are specific choices along each of these axes. We show via experiments that by combining feature engineering with embedding learning, our method can outperform CBOW using only 10% of the training data in both the standard word embedding evaluations and also text classification experiments.
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