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Lexical chain consists of cohesion words in a document, which implies theunderlying structure of a text, and thus facilitates downstream NLP tasks.Nevertheless, existing work focuses on detecting the simple surface lexiconswith shallow syntax associations, ignoring the semantic-aware lexical compoundsas well as the latent semantic frames, (e.g., topic), which can be much morecrucial for real-world NLP applications. In this paper, we introduce a noveltask, Nominal Compound Chain Extraction (NCCE), extracting and clustering allthe nominal compounds that share identical semantic topics. In addition, wemodel the task as a two-stage prediction (i.e., compound extraction and chaindetection), which is handled via a proposed joint framework. The model employsthe BERT encoder to yield contextualized document representation. Also, HowNetis exploited as external resources for offering rich sememe information. Theexperiments are based on our manually annotated corpus, and the results provethe necessity of the NCCE task as well as the effectiveness of our jointapproach.

Nominal Compound Chain Extraction: A New Task for Semantic-enriched Lexical Chain