Document clustering of scientific texts using citation contexts

Abstract

Document clustering has many important applications in the area of data mining and information retrieval. Many existing document clustering techniques use the ''bag-of-words'' model to represent the content of a document. However, this representation is only effective for grouping related documents when these documents share a large proportion of lexically equivalent terms. In other words, instances of synonymy between related documents are ignored, which can reduce the effectiveness of applications using a standard full-text document representation. To address this problem, we present a new approach for clustering scientific documents, based on the utilization of citation contexts. A citation context is essentially the text surrounding the reference markers used to refer to other scientific works. We hypothesize that citation contexts will provide relevant synonymous and related vocabulary which will help increase the effectiveness of the bag-ofwords representation. In this paper, we investigate the power of these citation-specific word features, and compare them with the original document's textual representation in a document clustering task on two collections of labeled scientific journal papers from two distinct domains: High Energy Physics and Genomics. We also compare these text-based clustering techniques with a link-based clustering algorithm which determines the similarity between documents based on the number of co-citations, that is in-links represented by citing documents and out-links represented by cited documents. Our experimental results indicate that the use of citation contexts, when combined with the vocabulary in the full-text of the document, is a promising alternative means of capturing critical topics covered by journal articles. More specifically, this document representation strategy when used by the clustering algorithm investigated in this paper, outperforms both the full-text clustering approach and the link-based clustering technique on both scientific journal datasets.

Improving document clustering using Okapi BM25 feature weighting

“…It is common in document clustering literature to use tf-idf weighting, mostly with length normalization (Slonim and Tishby 2000;Steinbach et al 2000;Zhao and Karypis 2002;Xu et al 2003;Zhao and Karypis 2004;Hu et al 2009;Aljaber et al 2010 to name a few such works). There is some research from fields related to clustering, such as classification, that indicate that idf is an important part of feature weighting for those fields, while tf is (surprisingly) not as useful (Wilbur and Kim 2009).…”

Spectral Clustering Wikipedia Keyword-Based Search Results

“…One of the approaches that combine the proposed above representations using a reference context has been shown in Aljaber et al (2010). The authors use the text surrounding the references to extend the text representation for document clustering.…”

Big Data Categorization for Arabic Text Using Latent Semantic Indexing and Clustering

“…Reference [7] describe two flat clustering algorithms: the K-Means algorithm, an efficient and widely used document clustering method, and the expectationmaximization algorithm, which is computationally more expensive, but also more flexible. Reference [8] presented an approach for clustering scientific documents based on the utilization of citation contexts. Reference [9] gave a brief overview of the document clustering research and the developments in this field.…”