  Recent research has shown great progress on fine-grained entity typing. Most
existing methods require pre-defining a set of types and training a multi-class
classifier from a large labeled data set based on multi-level linguistic
features. They are thus limited to certain domains, genres and languages. In
this paper, we propose a novel unsupervised entity typing framework by
combining symbolic and distributional semantics. We start from learning general
embeddings for each entity mention, compose the embeddings of specific contexts
using linguistic structures, link the mention to knowledge bases and learn its
related knowledge representations. Then we develop a novel joint hierarchical
clustering and linking algorithm to type all mentions using these
representations. This framework doesn't rely on any annotated data, predefined
typing schema, or hand-crafted features, therefore it can be quickly adapted to
a new domain, genre and language. Furthermore, it has great flexibility at
incorporating linguistic structures (e.g., Abstract Meaning Representation
(AMR), dependency relations) to improve specific context representation.
Experiments on genres (news and discussion forum) show comparable performance
with state-of-the-art supervised typing systems trained from a large amount of
labeled data. Results on various languages (English, Chinese, Japanese, Hausa,
and Yoruba) and domains (general and biomedical) demonstrate the portability of
our framework.
