TechTalks from event: NAACL 2015

5A: Semantics

  • So similar and yet incompatible: Toward the automated identification of semantically compatible words Authors: Germn Kruszewski and Marco Baroni
    We introduce the challenge of detecting semantically compatible words, that is, words that can potentially refer to the same thing (cat and hindrance are compatible, cat and dog are not), arguing for its central role in many semantic tasks. We present a publicly available data-set of human compatibility ratings, and a neural-network model that takes distributional embeddings of words as input and learns alternative embeddings that perform the compatibility detection task quite well.
  • Do Supervised Distributional Methods Really Learn Lexical Inference Relations? Authors: Omer Levy, Steffen Remus, Chris Biemann, Ido Dagan
    Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations between word pairs, such as hypernymy and entailment. We investigate a collection of these state-of-the-art methods, and show that they do not actually learn a relation between two words. Instead, they learn an independent property of a single word in the pair: whether that word is a ``prototypical hypernym''.
  • A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions Authors: Bahar Salehi, Paul Cook, Timothy Baldwin
    This paper presents the first attempt to use word embeddings to predict the compositionality of multiword expressions. We consider both single- and multi-prototype word embeddings. Experimental results show that, in combination with a back-off method based on string similarity, word embeddings outperform a method using count-based distributional similarity. Our best results are competitive with, or superior to, state-of-the-art methods over three standard compositionality datasets, which include two types of multiword expressions and two languages.
  • Word Embedding-based Antonym Detection using Thesauri and Distributional Information Authors: Masataka Ono, Makoto Miwa, Yutaka Sasaki
    This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, however, cannot capture antonyms since they depend on the distributional hypothesis. Our approach utilizes supervised synonym and antonym information from thesauri, as well as distributional information from large-scale unlabelled text data. The evaluation results on the GRE antonym question task show that our model outperforms the state-of-the-art systems and it can answer the antonym questions in the F-score of 89%.
  • A Comparison of Word Similarity Performance Using Explanatory and Non-explanatory Texts Authors: Lifeng Jin and William Schuler
    Vectorial representations derived from large current events datasets such as Google News have been shown to perform well on word similarity tasks. This paper shows vectorial representations derived from substantially smaller explanatory text datasets such as English Wikipedia and Simple English Wikipedia preserve enough lexical semantic information to make these kinds of category judgments with equal or better accuracy. Analysis shows these results are driven by a prevalence of commonsense facts in explanatory text. These positive results for small datasets suggest vectors derived from slower but more accurate deep parsers may be practical for lexical semantic applications.