The Probabilistic Relevance Framework: BM25 and Beyond

Robertson, Stephen; Zaragoza, Hugo

doi:10.1561/1500000019

Cited by 1,682 publications

(1,117 citation statements)

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“…We used a state-of-the-art information retrieval model, BM25 [21] as a baseline. The baseline model retrieved the documents using the original query containing the entity name without expansion (i.e.…”

Section: Precision-recall Analysismentioning

confidence: 99%

What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval

Zhou

Demidova

Cristea

2017

Transactions on Computational Collective Intelligence XXVI

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Section: Precision-recall Analysismentioning

confidence: 99%

What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval

Zhou

Demidova

Cristea

2017

Transactions on Computational Collective Intelligence XXVI

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“…a) Okapi BM25: To rank the final set of the retrieved documents, Okapi BM25 [11], [12] may be used as a ranking function. BM25 retrieval function ranks a set of documents based on the query terms appearing in each document, regardless of the inter-relationship between the query terms within a document.…”

Section: A Corpusmentioning

confidence: 99%

Cross-Language Information Retrieval with Incorrect Query Translations

Prasath

Sarkar

2016

Polibits

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Abstract-In this paper, we present a Cross Language Information Retrieval (CLIR) approach using corpus driven query suggestion. We have used corpus statistics to gather a clue on selecting the right query terms when the translation of a specific query is missing or incorrect. The derived set of queries are ranked to select the top ranked queries. These top ranked queries are further used to perform query formulation. Using the re-formulated weighted query, we perform cross language information retrieval. The results are compared with the results of CLIR system with Google translation of user queries and CLIR with the proposed query suggestion approach. We have English and Tamil corpus of FIRE 2012 dataset and analyzed the effects of the proposed approach. The experimental results show that the proposed approach performs well with the incorrect translation of the queries.

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“…Second, because the document vectors are very sparse, documents under the cosine measure are efficiently indexable by the inverted index. (12) here, N j is the top-N recommendation number for paper c j which is predefined by system or requested by user, in our paper, N j corresponds to our four settings as N j = {5, 10, 15, 20}. l, m ∈ N j is the index for papers in recommendation pool.…”

Section: Recommendation Correlationmentioning

confidence: 99%

Increasing Serendipity of Recommender System with Ranking Topic Model

Xiao¹,

Che²,

Miao³

et al. 2014

Appl. Math. Inf. Sci.

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There are thousands of academic paper published each year, it is quite hard for researchers who enters a new field to discover relevant paper and novel paper to read, which we characterize as choice overload problem. Recommender system can help to alleviate the problem, but recommender system suffers from the intention gap problem which is the incapability of the system to accurately guess users' intentions. We proposed a ranking topic model based semantic recommendation framework which helps to introduce serendipity to the system. First, the proposed ranking topic model reorders learnt topic distributions according to users' intentions. Then, learnt ordered topics are used as features to rank papers in the library according to the relevancy to user query. At the same time, ranked topics also provide novelty to the results. Since there is little work on how to evaluate the serendipity degree of recommender system, we proposed two measure to evaluate this metric. We performed empirical experiments to test the efficiency of proposed framework with state-of-the-art counterparts, the comparison results revealed the superiority of our proposed algorithms. In the end, we illustrated our algorithms with an example and pointed out future research directions.

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The Probabilistic Relevance Framework: BM25 and Beyond

Cited by 1,682 publications

References 0 publications

What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval

What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval

Cross-Language Information Retrieval with Incorrect Query Translations

Increasing Serendipity of Recommender System with Ranking Topic Model

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