Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm ...

An explanation for the acquisition of word-object mappings is the associative learning in a crosssituational scenario. Here we present analytical results of the performance of a simple associative learning algorithm for ...

Learning novel words is a challenging process for our memory systems; we must be able to recall new word forms and meanings in order to communicate. However, the dynamics of the word memory formation is still unclear. Here, ...

This work explores the use of word embeddings as features for Spanish verb sense disambiguation (VSD). This type of learning technique is named disjoint semisupervised learning [21]: an unsupervised algorithm (i.e. the ...

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two ...

Unsupervised features based on word representations such as word embeddings and word collocations have shown to significantly improve supervised NER for English. In this work we investigate whether such unsupervised features ...

In the present study we explored the postlearning changes in a novel word’s definition using a cue-induced memory reactivation. Native speakers of Spanish (N = 373) learned low-frequency words with their corresponding ...

Purpose: To develop a database of words of high, medium and low frequency in reading for Basic Education II. Methods: The words were taken from the teaching material for Portuguese Language, used by the teaching network ...