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Entropy and Graph Based Modelling of Document Coherence using Discourse Entities: An Application to IR
| Content Provider | CiteSeerX |
|---|---|
| Author | Petersen, Casper Lioma, Christina Simonsen, Jakob Grue Larsen, Birger |
| Abstract | We present two novel models of document coherence and their application to information retrieval (IR). Both models approximate document coherence using discourse entities, e.g. the subject or object of a sentence. Our first model views text as a Markov process generating sequences of dis-course entities (entity n-grams); we use the entropy of these entity n-grams to approximate the rate at which new infor-mation appears in text, reasoning that as more new words appear, the topic increasingly drifts and text coherence de-creases. Our second model extends the work of Guinaudeau & Strube [28] that represents text as a graph of discourse entities, linked by different relations, such as their distance or adjacency in text. We use several graph topology metrics to approximate different aspects of the discourse flow that can indicate coherence, such as the average clustering or be-tweenness of discourse entities in text. Experiments with several instantiations of these models show that: (i) our models perform on a par with two other well-known models of text coherence even without any parameter tuning, and (ii) reranking retrieval results according to their coherence scores gives notable performance gains, confirming a rela-tion between document coherence and relevance. This work contributes two novel models of document coherence, the application of which to IR complements recent work in the integration of document cohesiveness or comprehensibility to ranking [5, 56]. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
