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. (shrink)

Lexical ambiguity—the phenomenon of a single word having multiple, distinguishable senses—is pervasive in language. Both the degree of ambiguity of a word (roughly, its number of senses) and the relatedness of those senses have been found to have widespread effects on language acquisition and processing. Recently, distributional approaches to semantics, in which a word's meaning is determined by its contexts, have led to successful research quantifying the degree of ambiguity, but these measures have not distinguished between the ambiguity of words (...) with multiple related senses versus multiple unrelated meanings. In this work, we present the first assessment of whether distributional meaning representations can capture the ambiguity structure of a word, including both the number and relatedness of senses. On a very large sample of English words, we find that some, but not all, distributional semantic representations that we test exhibit detectable differences between sets of monosemes (unambiguous words; N = 964), polysemes (with multiple related senses; N = 4,096), and homonyms (with multiple unrelated senses; N = 355). Our findings begin to answer open questions from earlier work regarding whether distributional semantic representations of words, which successfully capture various semantic relationships, also reflect fine‐grained aspects of meaning structure that influence human behavior. Our findings emphasize the importance of measuring whether proposed lexical representations capture such distinctions: In addition to standard benchmarks that test the similarity structure of distributional semantic models, we need to also consider whether they have cognitively plausible ambiguity structure. (shrink)

The best defence against the suggestion that Hume’s use of the laws of nature is question-begging is the both-sides-need-the-laws’ response in its variations. Efforts along these lines by Antony Flew, J L Mackie, and more recently J C Thornton are shown to fail. Hume intends to rule out miracles by ruling out, e.g., resurrections, not just rule out calling resurrections miracles’. The both-sides-need-the-laws’ objection can target only the latter and it fails to do even this.

We conducted a case study on how unreliable and/or unrepresentative stimuli in psycholinguistics research may impact the generalizability of experimental findings. Using the domain of lexical ambiguity as a foil, we analyzed 2033 unique words (6481 tokens) from 214 studies. Specifically, we examined how often studies agreed on the ambiguity types assigned to a word (i.e., homonymy, polysemy, and monosemy), and how well the words represented the populations underlying each ambiguity type. We observed far from perfect agreement in terms of (...) how words are assigned to ambiguity types. We also observed that coverage of the populations is relatively poor and biased, leading to the use of a narrower set of words and associated properties. This raises concerns about the degree to which prior theoretical claims have strong empirical support, and offers targeted directions to improve research practices that are relevant to a broad set of domains. (shrink)

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 context embedding model, here exemplified by BERT, represents regular polysemy, we analyzed whether its embeddings support answering sense analogy questions similar to “is the mapping be- tween CHICKEN (as an animal) and CHICKEN (as a meat) the same as that which maps between LOBSTER (as an animal) to (...) LOBSTER (as a meat)?” We found that (1) the model was sensitive to the shared structure within a regularity type; (2) the shared structure varies across regularity types, potentially reflective of a “regularity continuum;” (3) some high-order latent structure may be shared across regularity types, suggestive of a similar la- tent structure across types; and (4) there is equivocal ev- idence that the aforementioned effects are explained by meaning overlap. (shrink)

The Monographs produced by the International Agency for Research on Cancer (IARC) apply rigorous procedures for the scientific review and evaluation of carcinogenic hazards by independent experts. The Preamble to the IARC Monographs, which outlines these procedures, was updated in 2019, following recommendations of a 2018 expert Advisory Group. This article presents the key features of the updated Preamble, a major milestone that will enable IARC to take advantage of recent scientific and procedural advances made during the 12 years since (...) the last Preamble amendments. The updated Preamble formalizes important developments already being pioneered in the Monographs Programme. These developments were taken forward in a clarified and strengthened process for identifying, reviewing, evaluating and integrating evidence to identify causes of human cancer. The advancements adopted include strengthening of systematic review methodologies; greater emphasis on mechanistic evidence, based on key characteristics of carcinogens; greater consideration of quality and informativeness in the critical evaluation of epidemiological studies, including their exposure assessment methods; improved harmonization of evaluation criteria for the different evidence streams; and a single-step process of integrating evidence on cancer in humans, cancer in experimental animals and mechanisms for reaching overall evaluations. In all, the updated Preamble underpins a stronger and more transparent method for the identification of carcinogenic hazards, the essential first step in cancer prevention. (shrink)