Learning Semantic Hierarchies via Word Embeddings

By Ruiji Fu, Jiang Guo, Bing Qin, Wanxiang Che, Haifeng Wang, Ting-Wei Liu. 2014

290 citations recorded by the source corpus.

Semantic hierarchy construction aims to build structures of concepts linked by hypernym‐hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of such relations. This paper proposes a novel and effective method for the construction of semantic hierarchies based on word embeddings, which can be used to measure the semantic relationship between words. We identify whether a candidate word pair has hypernym‐hyponym relation by using the word-embedding-based semantic projections between words and their hypernyms. Our result, an F-score of 73.74%, outperforms the state-of-theart methods on a manually labeled test dataset. Moreover, combining our method with a previous manually-built hierarchy extension method can further improve Fscore to 80.29%.

Subfields: Artificial Intelligence, Natural Language Processing, Representation & Self-Supervised Learning

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Citations in the corpus

Experience Grounds Language (2020, background): “…aforementioned approaches for learning transferable representations demonstrate that sentence and document context provide powerful signals for learning aspects of meaning, especially semantic relations among words (Fu et al., 2014) and inferential relationships among sentences (Wang et al., 2018).”
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings (2018, background): “…as link prediction in knowledge bases (Nickel et al., 2011; Bor-des et al., 2013) or in social networks (Hoff et al., 2002), text disambiguation (Ganea & Hofmann, 2017), word hyper-nymy (Shwartz et al., 2016), textual entailment (Rockt¨aschel et al., 2015) or taxonomy induction (Fu et al., 2014).”
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts (2017, background): “…as Named Entity Recognition (Lample et al. 2016), (Santos and Guimaraes 2015), syntactic parsing (Bansal, Gimpel, and Livescu 2014), semantic relation extraction (Nguyen and Grishman 2015), (Fu et al. 2014) and sentiment analysis (Maas et al. 2011), (Tang et al. 2014), to boost their performance.”
Intent detection using semantically enriched word embeddings (2016, methodology): “However, these enrichment approaches showed improved performance only on word-level tasks such as word similarity [8, 9, 11], antonym detection [10, 11, 16, 13], semantic hierarchies [12, 15], and semantic scale inference [14].”
The (too Many) Problems of Analogical Reasoning with Word Vectors (2017, background): “in taxonomy construction it was found helpful to narrow the semantic space with domains or clusters, essentially “zooming in” on certain relations (Fu et al., 2014; Espinosa Anke et al., 2016).”
Do Supervised Distributional Methods Really Learn Lexical Inference Relations? (2015, methodology): “(2012) use concatenation ~x ⊕ ~y, others (Roller et al., 2014; Weeds et al., 2014; Fu et al., 2014) take the vectors’ difference ~y − ~x, and more sophisticated representations, based on contextual features, have also been tested (Turney and Mohammad, 2014; Rimell, 2014).”