Short Text Topic Learning Using Heterogeneous Information Network

With the explosive growth of short texts on users’ interests and preferences, learning discriminative and coherent latent topics from short texts is a critical and significative work, since many practical applications, such as e-commerce and recommendations, require semantic understandings that short texts convey explicitly and implicitly. However, existing short text topic learning methods face the challenge of fully capturing semantically related co-occurrence phrases. Therefore, this paper proposes a novel
H
eterogeneous
I
nformation
N
etwork-based
Sho
rt
T
ext
T
opic learning approach (HIN-ShoTT) in terms of parts of speech, without depending on any auxiliary information. Specifically, HIN-ShoTT can be decomposed into three phases:
${{i}}$
) seeking semantic relations among words with different parts of speech, where HIN-ShoTT models multiple explicit and implicit semantic relations among words based on a Heterogeneous Information Network (HIN) in terms of parts of speech;
${{ii}}$
) extracting co-occurrence phrases and filtering noises, where HIN-ShoTT defines parts-of-speech meta structures to guide co-occurrence phrase extraction and a self-adapting threshold filtering module is proposed for discarding noises; and
${{iii}}$
) inferring topics, where HIN-ShoTT directly models the generative process of co-occurrence phrases to make topic learning effective with the abundant corpus-level information. Our experimental results on three real-world datasets not only show that HIN-ShoTT performs well, but also demonstrate that it is feasible to incorporate HIN into short text topic learning for accuracy improvement.