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