Learning words and syntactic cues in highly ambiguous contexts
The cross-situational word learning paradigm argues that word meanings can be approximated by word-object associations, computed from co-occurrence statistics between words and entities in the world. Lexicon acquisition involves simultaneously guessing (1) which objects are being talked about (the âmeaningâ) and (2) which words relate to those objects. However, most modeling work focuses on acquiring meanings for isolated words, largely neglecting relationships between words or physical entities, which can play an important role in learning. Semantic parsing, on the other hand, aims to learn a mapping between entire utterances and compositional meaning representations where such relations are central. The focus is the mapping between meaning and words, while utterance meanings are treated as observed quantities. Here, we extend the joint inference problem of word learning to account for compositional meanings by incorporating a semantic parsing model for relating utterances to non-linguistic context. Integrating semantic parsing and word learning permits us to explore the impact of word-word and concept-concept relations. The result is a joint-inference problem inherited from the word learning setting
Jones, Bevan Keeley
word learning ; semantic parsing ; computational linguistics ; computational modeling ; graph grammar ; frog stories ; variational Bayes
Learning words and syntactic cues in highly ambiguous contexts. The cross-situational word learning paradigm argues that word meanings can be approximated by word-object associations, computed from co-occurrence statistics between words and entities in the world. Lexicon acquisition involves simultaneously guessing (1) which objects are being talked about (the âmeaningâ) and (2) which words relate to those objects. However, most modeling work focuses on acquiring meanings for isolated words, largely neglecting relationships between words or physical entities, which can play an important role in learning. Semantic parsing, on the other hand, aims to learn a mapping between entire utterances and compositional meaning representations where such relations are central. The focus is the mapping between meaning and words, while utterance meanings are treated as observed quantities. Here, we extend the joint inference problem of word learning to account for compositional meanings by incorporating a semantic parsing model for relating utterances to non-linguistic context. Integrating semantic parsing and word learning permits us to explore the impact of word-word and concept-concept relations. The result is a joint-inference problem inherited from the word learning setting
learning word syntactic cue highly ambiguous context cross situational word learn paradigm argues word_meaning approximate word object association compute co_occurrence statistic word entity world lexicon acquisition involve simultaneously guess object talk mean word relate object however model work focus acquire meaning isolate word largely_neglect relationship word physical entity play_important_role learning semantic parsing hand aim learn map entire utterance compositional meaning_representation relation central focus map meaning word utterance meaning treat observed quantity extend joint inference problem word learn account compositional meaning incorporate semantic parsing model relate utterance non_linguistic context integrating semantic parsing word learn permit explore_impact word word concept concept relation joint inference problem inherit word learn set