Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization

By Lucie Flekova, Iryna Gurevych. 2016

60 citations recorded by the source corpus.

Coarse-grained semantic categories such as supersenses have proven useful for a range of downstream tasks such as question answering or machine translation. To date, no effort has been put into integrating the supersenses into distributional word representations. We present a novel joint embedding model of words and supersenses, providing insights into the relationship between words and supersenses in the same vector space. Using these embeddings in a deep neural network model, we demonstrate that the supersense enrichment leads to a significant improvement in a range of downstream classification tasks.

Subfields: Natural Language Processing

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Citations in the corpus

WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (2018, methodology): “This coarsening of the WordNet sense inventory has been shown particularly useful in downstream applications (R¨ud et al., 2011; Severyn et al., 2013; Flekova and Gurevych, 2016; Pilehvar et al., 2017).”
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning (2018, methodology): “The major beneﬁts of using sense representations were observed when using supersenses (see Section 7.3), a conclusion which was also observed by Flekova and Gurevych (2016) on other downstream classiﬁcation tasks.”
Neural Sequence Learning Models for Word Sense Disambiguation (2017, background): “Recently, WSD has also been leveraged to build continuous vector representations for word senses (Chen et al., 2014; Iacobacci et al., 2015; Flekova and Gurevych, 2016).”
WiC: 10, 000 Example Pairs for Evaluating Context-Sensitive Representations (2018, background): “been shown particularly useful in downstream applications (Rüd et al., 2011; Severyn et al., 2013; Flekova and Gurevych, 2016; Pilehvar et al., 2017).”
Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017, background): “WordNet or Wikipedia, which corroborates previous findings (Hovy et al., 2013; Flekova and Gurevych, 2016).”
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets (2017, methodology): “C ONVOLUTIONAL N EURAL N ETWORKS (C NN ) have proven effective for text classiﬁcation (dos Santos and Gatti, 2014; Kim, 2014; Flekova and Gurevych, 2016).”
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training (2016, background): “In fact, a meaningful clustering of their senses may lead to improvements on downstream tasks (Hovy et al., 2013; Flekova and Gurevych, 2016; Pilehvar et al., 2017).”