Information Retrieval by Document Re-ranking using Term Association Graph

Veningston, K.; Shanmugalakshmi, R.

doi:10.1145/2660859.2660927

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Li et al introduced a document re-ranking using partial social tagging [22] which is the main limitation of the approach. Veningston and Shanmugalakshmi proposed to exploit term graph data structure and re-rank documents according to the association and similarity between them [23]. The authors stated that their approach involve expensive computation.…”

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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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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“…If two terms point to each other but to no other terms, this loop will accumulate rank but never distribute rank to other terms during the iteration. This approach has been described in detail in Veningston & Shanmugalakshmi (2014).…”

Section: Rank(t Amentioning

confidence: 99%

Semantic association ranking schemes for information retrieval applications using term association graph representation

Veningston¹,

Shanmugalakshmi²,

Nirmala³

2015

Sadhana

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Most of the Information Retrieval (IR) techniques are based on representing the documents using the traditional vector space and probabilistic language model i.e., bag-of-words model. In this paper, associations among words in the documents are assessed and it is expressed in Term Association Graph model to represent the document content and the relationship among the keywords. Earlier attempt on exploiting term association graph was done for non-personalized document re-ranking task. This paper experiments improved non-personalized and personalized re-ranking strategy which exploits term association graph data structure to assess the importance of a document for the user query and thus documents are re-ranked according to the association and similarity exists among the documents. This paper proposes various approaches under two models namely, Term Rank based Approach (TRA) and Path Traversal based Approaches (PTA1, PTA2, and PTA3). These approaches employ term association graph and has been evaluated using manually prepared real dataset and benchmark OHSUMED dataset. The results obtained are reasonably promising.

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“…In [6] an unsupervised clustering technique called SOPHIA is presented, that is evaluated on the MEDLINE testing set collection. Study [7] describes an experiment that changes the ranking strategy using the term-graph data structure for assessing the importance of a document to a user's query to the MEDLINE database. In [8] existent question-answering system based on principles of evidence based medicine is presented.…”

Section: Introductionmentioning

confidence: 99%

The data retrieval optimization from the perspective of evidence-based medicine

Dobrynin¹,

Balykina²,

Kamalov³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-The paper is devoted to classification of MEDLINE abstracts into categories that correspond to types of medical interventions -types of patient treatments. This set of categories was extracted from Clinicaltrials.gov web site. Few classification algorithms were tested including Multinomial Naive Bayes, Multinomial Logistic Regression, and Linear SVM implementations from sklearn machine learning library. Document marking was based on the consideration of abstracts containing links to the Clinicaltrials.gov Web site. As the result of an automatical marking 3534 abstracts were marked for training and testing the set of algorithms metioned above. Best result of multinomial classification was achieved by Linear SVM with macro evaluation precision 70.06%, recall 55.62% and F-measure 62.01%, and micro evaluation precision 64.91%, recall 79.13% and F-measure 71.32%.

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Information Retrieval by Document Re-ranking using Term Association Graph

Cited by 3 publications

References 24 publications

Document re-ranking based on topic-comment structure

Document re-ranking based on topic-comment structure

Semantic association ranking schemes for information retrieval applications using term association graph representation

The data retrieval optimization from the perspective of evidence-based medicine

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