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Examples of context and definitions of WSD-model predicted senses. The bold italic words in context are disambiguated by the BEM model before and after training on WSE.
Source publication
Humans often make creative use of words to express novel senses. A long-standing effort in natural language processing has been focusing on word sense disambiguation (WSD), but little has been explored about how the sense inventory of a word may be extended toward novel meanings. We present a paradigm of word sense extension (WSE) that enables word...
Contexts in source publication
Context 1
... observe that all WSD models trained on WSE yield substantially greater improvement for few-shot and zero-shot test cases, while maintaining high performance on the more frequent cases. Table 4 shows test examples where incorrect predictions of BEM are improved with WSE integration. These examples often exhibit regular semantic relations between target and conventional senses of a word (e.g., the relation between physical size and amount that underlies the two attested senses of full). ...Context 2
... observe that all WSD models trained on WSE yield substantially greater improvement for few-shot and zero-shot test cases, while maintaining high performance on the more frequent cases. Table 4 shows test examples where incorrect predictions of BEM are improved with WSE integration. These examples often exhibit regular semantic relations between target and conventional senses of a word (e.g., the relation between physical size and amount that underlies the two attested senses of full). ...Similar publications
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Citations
Regular polysemes are sets of ambiguous words that all share the same relationship between their meanings, such as CHICKEN and LOBSTER both referring to an animal or its meat. To probe how a distributional semantic model, here exemplified by bidirectional encoder representations from transformers (BERT), represents regular polysemy, we analyzed whether its embeddings support answering sense analogy questions similar to “is the mapping between CHICKEN (as an animal) and CHICKEN (as a meat) similar to that which maps between LOBSTER (as an animal) to LOBSTER (as a meat)?” We did so using the LRcos model, which combines a logistic regression classifier of different categories (e.g., animal vs. meat) with a measure of cosine similarity. We found that (a) the model was sensitive to the shared structure within a given regular relationship; (b) the shared structure varies across different regular relationships (e.g., animal/meat vs. location/organization), potentially reflective of a “regularity continuum;” (c) some high‐order latent structure is shared across different regular relationships, suggestive of a similar latent structure across different types of relationships; and (d) there is a lack of evidence for the aforementioned effects being explained by meaning overlap. Lastly, we found that both components of the LRcos model made important contributions to accurate responding and that a variation of this method could yield an accuracy boost of 10% in answering sense analogy questions. These findings enrich previous theoretical work on regular polysemy with a computationally explicit theory and methods, and provide evidence for an important organizational principle for the mental lexicon and the broader conceptual knowledge system.
