LDA Meets Word2Vec: A Novel Model for Academic Abstract Clustering.

Clustering narrow-domain short texts, such as academic abstracts, is an extremely difficult clustering problem. Firstly, short texts lead to low frequency and sparseness of words, making clustering results highly unstable and inaccurate; Secondly, narrow domain leads to great overlapping of insignificant words and makes it hard to distinguish between sub-domains, or fine-grained clusters. The vocabulary size is also too small to construct a good word bag needed by traditional clustering algorithms like LDA to give a meaningful topic distribution. A novel clustering model, Partitioned Word2Vec-LDA (PW-LDA), is proposed in this paper to tackle the described problems. Since the purpose sentences of an abstract contain crucial information about the topic of the paper, we firstly implement a novel algorithm to extract them from the abstracts according to its structural features. Then high-frequency words are removed from those purpose sentences to get a purified-purpose corpus and LDA and Word2Vec models are trained. After combining the results of both models, we can cluster the abstracts more precisely. Our model uses abstract text instead of keywords to cluster because keywords may be ambiguous and cause unsatisfied clustering results shown by previous work. Experimental results show that the clustering results of PW-LDA are much more accurate and stable than state-of-the-art techniques.