Bigger versus Similar: Selecting a Background Corpus for First Story Detection Based on Distributional Similarity
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The current state of the art for First Story Detection (FSD) are nearest neighbour-based models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus. Consequently, the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution. However, given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale of common terms for FSD. As a basis for our analysis we propose a set of metrics to quantitatively measure the scale of common terms and the distributional similarity between corpora. Using these metrics we rank different background corpora relative to a target corpus. We also apply models based on different background corpora to the FSD task. Our results show that term distributional similarity is more predictive of good FSD performance than the scale of common terms; and, thus we demonstrate that a smaller recent domain-related corpus will be more suitable than a very large-scale general corpus for FSD.
Wang, F., Ross, R. & Kelleher, J. (2019). Bigger versus similar: selecting a background corpus for first story detection based on distributional similarity. RANLP-2019 Summer School on deep learning in NLP: Recent Advances in Nature Language Processing , Varna, Bulgaria, 29-30 August.
ADAPT Reseach Centre
RANLP-2019 Summer School on deep learning in NLP: Recent Advances in Nature Language Processing