Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments ("How similar are cats and bears?"), and how these judgments…

Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one-to-many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic…

This paper explores the processes underlying verb metaphoric extension. Work on metaphor processing has largely focused on noun metaphor, despite evidence that verb metaphor is more common. Across three experiments, we collected paraphrases of simple intransitive sentences varying in semantic strain--for example, "The motor complained"…

The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented…

Distributional models of semantics learn word meanings from contextual co-occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co-occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co-occurrences…

We explore the following two cognitive questions regarding crosslinguistic variation in lexical semantic systems: Why are some linguistic categories--that is, the associations between a term and a portion of the semantic space--harder to learn than others? How does learning a language-specific set of lexical categories affect processing in that…

What determines human ratings of association? We planned this paper as a test for association strength (AS) that is derived from the log likelihood that two words co-occur significantly more often together in sentences than is expected from their single word frequencies. We also investigated the moderately correlated interactions of word…

What is the relation between factual conditionals: "If A happened then B happened," and counterfactual conditionals: "If A had happened then B would have happened?" Some theorists propose quite different semantics for the two. In contrast, the theory of mental models and its computer implementation interrelates them. It…

Multiple constraints, including semantic, lexical, and usage-based factors, have been shown to influence dative alternation across different languages. This work explores whether fine-grained statistics and semantic properties of the verb affect the acceptability of dative constructions in Spanish. First, a corpus analysis reveals that verbs of…

Ten years ago, researchers using event-related brain potentials (ERPs) to study language comprehension were puzzled by what looked like a "Semantic Illusion": Semantically anomalous, but structurally well-formed sentences did not affect the N400 component--traditionally taken to reflect semantic integration--but instead produced a P600…

Natural languages make prolific use of conventional constituent-ordering patterns to indicate "who did what to whom," yet the mechanisms through which these regularities arise are not well understood. A series of recent experiments demonstrates that, when prompted to express meanings through silent gesture, people bypass native language…

The organization of our knowledge about the world into an interconnected network of concepts linked by relations profoundly impacts many facets of cognition, including attention, memory retrieval, reasoning, and learning. It is therefore crucial to understand how organized semantic representations are acquired. The present experiment investigated…

Semantic categories in the world's languages often reflect a historical process of "chaining": A name for one referent is extended to a conceptually related referent, and from there on to other referents, producing a chain of exemplars that all bear the same name. The beginning and end points of such a chain might in principle be rather…

We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed "Pursuit," uses an associative…

Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of…