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.

In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.

We consider the task of predicting lexical entailment using distributional vectors. We perform a novel qualitative analysis of one existing model which was previously shown to only measure the prototypicality of word pairs. We find that the model strongly learns to identify hypernyms using Hearst patterns, which are well known to be predictive of lexical relations. We present a novel model which exploits this behavior as a method of feature extraction in an iterative procedure similar to Principal Component Analysis. Our model combines the extracted features with the strengths of other proposed models in the literature, and matches or outperforms prior work on multiple data sets.

Taxonomies hierarchically organize concepts in a domain. Building and maintaining them by hand is a tedious and time-consuming task. This paper proposes a novel, unsupervised algorithm for automatically learning an IS-A taxonomy from scratch by analyzing a given text corpus. Our approach is designed to deal with infrequently occurring concepts, so it can effectively induce taxonomies even from small corpora. Algorithmically, the approach makes two important contributions. First, it performs inference based on clustering and the distributional semantics, which can capture links among concepts never mentioned together. Second, it uses a novel graph-based algorithm to detect and remove incorrect is-a relations from a taxonomy. An empirical evaluation on five corpora demonstrates the utility of our proposed approach.