A. Nematzadeh

Vector-space models of semantics represent words as continuously-valued vectors and measure similarity based on the distance or angle between those vectors. Expand

A Computational Study of Late Talking in Word-Meaning Acquisition Aida Nematzadeh , Afsaneh Fazly , and Suzanne Stevenson Department of Computer Science University of Toronto {aida,afsANEh,suzanne}@cs.toronto.edu Abstract and ambiguous context. Expand

We use visual grounding to improve unsupervised word mapping between languages by learning embeddings from unpaired instructional videos narrated in the native language. Expand

We take a computational approach to investigate how the information present during each observation in a cross-situational framework can affect the overall acquisition of word meanings in various long-term word learning scenarios. Expand

We propose a new dataset for evaluating question answering models with respect to their capacity to reason about beliefs. Expand

We show that it is plausible to learn rich representations from naturalistic data for which a very simple search algorithm (a random walk) can replicate the human patterns. Expand

We present an algorithm for simultaneously learning word meanings and gradually growing a semantic network, which adheres to the cognitive plausibility requirements of incrementality and limited computations. Expand

We use a compu- tational model of word learning to study how individual dif- ferences between LTs and NDs give rise to differences in ab- stract knowledge of categories emerging from learned words, and how this affects their subsequent word learning. Expand

We build an incremental probabilistic computational model of word learning that incorporates a forgetting and attentional mechanism. Expand

We show that simple statistics based on the linguistic properties of multiword verbs are informative for identifying them in a corpus of child-directed utterances, and that such statistics can be used within a word learning model to learn associations between meanings and sequences of words. Expand