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- W2056458023 abstract "Document representation is a fundamental problem for text mining. Many efforts have been done to generate concise yet semantic representation, such as bag-of-words, phrase, sentence and topic-level descriptions. Nevertheless, most existing techniques counter difficulties in handling monolingual comparable corpus, which is a collection of monolingual documents conveying same topic. In this paper, we propose use of frame, a high-level semantic unit, and construct frame-based representations to semantically describe documents by bags of frames, using an information network approach. One major challenge in this representation is that semantically similar frames may be of different forms. For example, leaked in one news article can appear as the level of radiation increased in another article. To tackle problem, a text-based information network is constructed among frames and words, and a link-based similarity measure called SynRank is proposed to calculate similarity between frames. As a result, different variations of semantically similar frames are merged into a single descriptive frame using clustering, and a document can then be represented as a bag of representative frames. It turns out that frame-based document representation not only is more interpretable, but also can facilitate other text analysis tasks such as event tracking effectively. We conduct both qualitative and quantitative experiments on three comparable news corpora, to study effectiveness of frame-based document representation and similarity measure SynRank, respectively, and demonstrate that superior performance of frame-based document representation on different real-world applications." @default.
- W2056458023 created "2016-06-24" @default.
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- W2056458023 date "2013-12-01" @default.
- W2056458023 modified "2023-10-16" @default.
- W2056458023 title "Semantic Frame-Based Document Representation for Comparable Corpora" @default.
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- W2056458023 doi "https://doi.org/10.1109/icdm.2013.99" @default.
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