Recognizing lexical inferences is one of the building blocks of natural language understanding. Lexical inference corresponds to a semantic relation that holds between two lexical items (words and multi-word expressions), when the meaning of one can be inferred from the other. In reading comprehension, for example, answering the question "which phones have long-lasting batteries?" given the text "Galaxy has a long-lasting battery", requires knowing that Galaxy is a model of a phone. In text summarization, lexical inference can help identifying redundancy, when two candidate sentences for the summary differ only in terms that hold a lexical inference relation (e.g. "the battery is long-lasting" and "the battery is enduring").
In this talk, I will present our work on automatic acquisition of lexical semantic relations from free text, focusing on two methods: the first is an integrated path-based and distributional method for recognizing lexical semantic relations (e.g. cat is a type of animal, tail is a part of cat). The second method focuses on the special case of interpreting the implicit semantic relation that holds between the constituent words of a noun compound (e.g. olive oil is made of olives, while baby oil is for babies).
Vered is a Computer Science PhD student in the Natural Language Processing lab at Bar-Ilan University, under the supervision of Prof. Ido Dagan. Her research focuses on recognizing lexical semantic relations between words and phrases. She has worked on ontological relationships e.g., cat is a type of animal, tail is a part of cat; interpreting noun-compounds, e.g. olive oil is oil made of olives while baby oil is oil for babies; and identifying predicate paraphrases, e.g. that X died at Y may have the same meaning as X lived until Y in certain contexts. She completed her B.Sc. (2013) and M.Sc. (2015) in Computer Science in Bar-Ilan University.