In this article, I will mostly concentrate on the Hearst patterns, implementation and usage for hypernym extraction. However, I will use Named Entity Recognition (NER) and a dataset of patents; so I recommend checking my previous post in this cycle. Why do we care about patterns in the context of NLP? Because they significantly reduce and simplifies work, basically, it is a simple model. Despite being in the era of Transformer Neural Networks, patterns still can be beneficial.

In this article, I will mostly concentrate on the Hearst patterns, implementation and usage for hypernym extraction. However, I will use Named Entity Recognition (NER) and a dataset of patents; so I recommend checking my previous post in this cycle. Why do we care about patterns in the context of NLP? Because they significantly reduce and simplifies work, basically, it is a simple model. Despite being in the era of Transformer Neural Networks, patterns still can be beneficial.

The hyponym-hypernym relation is an essential element in the semantic network. Identifying the hypernym from a definition is an important task in natural language processing and semantic analysis. While a public dictionary such as WordNet works for common words, its application in domain-specific scenarios is limited. Existing tools for hypernym extraction either rely on specific semantic patterns or focus on the word representation, which all demonstrate certain limitations. Here we propose a method by combining both the syntactic structure in definitions given by the word's part of speech, and the bidirectional gated recurrent unit network as the learning kernel. The output can be further tuned by including other features such as a word's centrality in the hypernym cooccurrence network. The method is tested in the corpus from Wikipedia featuring definition with high regularity, and the corpus from Stack-Overflow whose definition is usually irregular. It shows enhanced performance compared with other tools in both corpora.