Rhetorical relations for information retrieval

Lioma, Christina; Larsen, Birger; Lu, Wei

doi:10.1145/2348283.2348407

Cited by 18 publications

(17 citation statements)

References 31 publications

(51 reference statements)

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“…Any other corpus of reasonable coverage can be used alternatively 3. Any other reliable term weighting score can be used alternatively, for instance any of the simpler[25,26,27] or more elaborate[28,29,30] formulations in the literature.…”

mentioning

confidence: 99%

A study of metrics of distance and correlation between ranked lists for compositionality detection

Lioma

Hansen

2017

Cognitive Systems Research

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Compositionality in language refers to how much the meaning of some phrase can be decomposed into the meaning of its constituents and the way these constituents are combined. Based on the premise that substitution by synonyms is meaning-preserving, compositionality can be approximated as the semantic similarity between a phrase and a version of that phrase where words have been replaced by their synonyms. Different ways of representing such phrases exist (e.g., vectors [1] or language models [2]), and the choice of representation affects the measurement of semantic similarity.We propose a new compositionality detection method that represents phrases as ranked lists of term weights. Our method approximates the semantic similarity between two ranked list representations using a range of well-known distance and correlation metrics. In contrast to most state-of-the-art approaches in compositionality detection, our method is completely unsupervised. Experiments with a publicly available dataset of 1048 human-annotated phrases shows that, compared to strong supervised baselines, our approach provides superior measurement of compositionality using any of the distance and correlation metrics considered.

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confidence: 99%

A study of metrics of distance and correlation between ranked lists for compositionality detection

Lioma

Hansen

2017

Cognitive Systems Research

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“…An even more problematic drawback is related to the shortcomings of the discourse parser since such parsers are very time consuming and cannot be applied on large volumes of data. Lioma et al state that topiccomment relations as defined by SPADE are extremely sparse in the benchmark IR collections [20], while in our approach topic-comment structure is common for all types of texts as well as for all genres.…”

Section: B Discourse-level Topic Vs Rhetorial Relations and Topiccommentioning

confidence: 90%

“…Lioma et al use rhetorical relations from SPADE parser to re-rank documents [20]. The authors introduced a query likelihood retrieval model based on the probability of generating the query terms from (1) a mixture of the probabilities of generating a query from a document and its rhetorical relations and (2) the probability of generating rhetorical relations from a document.…”

Section: B Discourse-level Topic Vs Rhetorial Relations and Topiccommentioning

confidence: 99%

Document re-ranking based on topic-comment structure

Ermakova

Mothe²

2016

2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)

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This paper introduces a novel approach for document re-ranking in information retrieval based on topic-comment structure of texts. While most information retrieval models make the assumption that relevant documents are about the query and that aboutness can be captured considering bags of words only, we rather consider a more sophisticated analysis of discourse to capture document relevance by distinguishing the topic of a text from what is said about the topic (comment) in the text. The topic-comment structure of texts is extracted automatically from the first retrieved documents which are then re-ranked so that the top documents are the ones that share their topics with the query. The evaluation on TREC collections shows that the method significantly improves the retrieval performance.

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“…Another common type of document quality approximations are content-based. These are numerous and diverse, including for instance, ratios of information-to-noise, of stopwords per document, or of document words per stopword list [4,46,47]; average term length per document [17]; term part-ofspeech [25,26]; ratio of technical terminology per (scientific) document [20]; ratio of non-compositional phrases per document [29]; syllable, term and/or sentence statistics [37] as per standard readability indices [8,15,19,27,28]; discourse structure [24]; document entropy computed from terms [4] or discourse entities [34]. The lexical or syntactic features used in the above content-based document quality approximations are assumed to indicate syntactic or semantic difficulty.…”

Section: Related Workmentioning

confidence: 99%

Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval

Lioma

Tarissan

Simonsen

et al. 2016

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval

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Document coherence describes how much sense text makes in terms of its logical organisation and discourse flow. Even though coherence is a relatively difficult notion to quantify precisely, it can be approximated automatically. This type of coherence modelling is not only interesting in itself, but also useful for a number of other text processing tasks, including Information Retrieval (IR), where adjusting the ranking of documents according to both their relevance and their coherence has been shown to increase retrieval effectiveness [34,37].The state of the art in unsupervised coherence modelling represents documents as bipartite graphs of sentences and discourse entities, and then projects these bipartite graphs into one-mode undirected graphs. However, one-mode projections may incur significant loss of the information present in the original bipartite structure. To address this we present three novel graph metrics that compute document coherence on the original bipartite graph of sentences and entities. Evaluation on standard settings shows that: (i) one of our coherence metrics beats the state of the art in terms of coherence accuracy; and (ii) all three of our coherence metrics improve retrieval effectiveness because, as closer analysis reveals, they capture aspects of document quality that go undetected by both keyword-based standard ranking and by spam filtering. This work contributes document coherence metrics that are theoretically principled, parameter-free, and useful to IR.

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Rhetorical relations for information retrieval

Cited by 18 publications

References 31 publications

A study of metrics of distance and correlation between ranked lists for compositionality detection

A study of metrics of distance and correlation between ranked lists for compositionality detection

Document re-ranking based on topic-comment structure

Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval

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