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Topic Difficulty Prediction in Entity Ranking
[chapter]
2009
Lecture Notes in Computer Science
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In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. ...
Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking. ...
Acknowledgements Most of this work was completed while Vladimir Naumovski was doing his internship at INRIA in 2008. ...
doi:10.1007/978-3-642-03761-0_29
fatcat:qggfph7cmzdidoxssqxql7sg6m
Estimating query difficulty for news prediction retrieval
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
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In this paper, we study how to determine the difficulty in retrieving predictions for a given news story. ...
More precisely, we address the query difficulty estimation problem for news prediction retrieval. ...
Thus, we take into account prediction robustness by employing several ranking models (cf. Section 4) in determining the difficulty of a given query or topic. ...
doi:10.1145/2396761.2398707
dblp:conf/cikm/KanhabuaN12a
fatcat:63mf66ejinafzkmpqmoxapfpui
Predicting the effectiveness of keyword queries on databases
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
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Keyword query interfaces (KQIs) for databases provide easy access to data, but often suffer from low ranking quality, i.e. low precision and/or recall, as shown in recent benchmarks. ...
We evaluate our query difficulty prediction model against two relevance judgment benchmarks for keyword search on databases, INEX and SemSearch. ...
They compare the probability distribution of topics in the top ranked documents with the probability distribution of topics of the whole collection to predict the degree of the difficulty of the query. ...
doi:10.1145/2396761.2398422
dblp:conf/cikm/ChengTH12
fatcat:mqya5hogvfexlcm5nmcaaqrrze
Efficient Prediction of Difficult Keyword Queries over Databases
2014
IEEE Transactions on Knowledge and Data Engineering
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We evaluate our query difficulty prediction model against two effectiveness benchmarks for popular keyword search ranking methods. ...
Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. ...
They compare the probability distribution of topics in the top ranked documents with the probability distribution of topics of the whole collection to predict the degree of the difficulty of the query. ...
doi:10.1109/tkde.2013.140
fatcat:psn3jdopkzewrb2xao2grpt7fi
Novel Approach for Predicting Difficult Keyword Queries over Databases using Effective Ranking
2015
International Journal of Engineering Research and
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In the existing work, analyses the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query in excess of a database, considering both the structure ...
In order to overcome these drawbacks, we are proposing the improved ranking algorithm which is used to enhance the accuracy rate of the system. ...
Our proposed query difficulty prediction model falls in this category Some methods use machine learning techniques to learn the properties of difficult queries and predict their hardness. ...
doi:10.17577/ijertv4is030251
fatcat:jmm6kuoyuzeoliphug2xf2pahu
Characterizing web content, user interests, and search behavior by reading level and topic
2012
Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12
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entities in Web searchusers, websites, and queries. ...
To help improve our modeling and understanding of this diversity, we apply automatic text classifiers, based on reading difficulty and topic prediction, to estimate a novel type of profile for important ...
the topics or range of difficulty levels they cover. ...
doi:10.1145/2124295.2124323
dblp:conf/wsdm/KimCBD12
fatcat:bzp4wlh5mrhjhp3lv6gjf6zb2y
Improved Neural Relation Detection for Knowledge Base Question Answering
[article]
2017
arXiv
pre-print
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In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. ...
Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. ...
Sections 5.1 and 5.2 elaborate how our relation detection help to re-rank entities in the initial entity linking, and then those re-ranked entities enable more accurate relation detection. ...
arXiv:1704.06194v2
fatcat:uzenppnb2rd45c5mzbre2d5yx4
Capturing Global Informativeness in Open Domain Keyphrase Extraction
[article]
2021
arXiv
pre-print
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Further analyses reveal the significant advantages of JointKPE in predicting long and non-entity keyphrases, which are challenging for previous neural KPE methods. ...
JointKPE learns to rank keyphrases by estimating their informativeness in the entire document and is jointly trained on the keyphrase chunking task to guarantee the phraseness of keyphrase candidates. ...
Despite the challenge, JointKPE and its informative ranking version (Rank) significantly outperform other methods in predicting non-entity keyphrases. ...
arXiv:2004.13639v2
fatcat:zx53kxez7rdyxo4rq353iakdgm
Improved Neural Relation Detection for Knowledge Base Question Answering
2017
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question. ...
Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to make the two components enhance each other. ...
Sections 5.1 and 5.2 elaborate how our relation detection help to re-rank entities in the initial entity linking, and then those re-ranked entities enable more accurate relation detection. ...
doi:10.18653/v1/p17-1053
dblp:conf/acl/YuYHSXZ17
fatcat:2swegocdifegtdcbf6lqzmwqfu
Predicting event-relatedness of popular queries
2013
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13
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Our analysis shows that the number of named entities in search results and their appearances in Wikipedia are among the most discriminative features for query event-relatedness prediction. ...
In this paper, we identify 20 features including both contextual and temporal features from a small set of search results of a query and predict its event-relatedness. ...
Query clarity has been used to predict query difficulty and a large KL divergence indicates a clear and unambiguous query [2] . ...
doi:10.1145/2505515.2507853
dblp:conf/cikm/GhoreishiS13
fatcat:agypapk3bngm7j3y3zjn725vky
Text Readability Assessment for Second Language Learners
2016
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications
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One of the major challenges in this task is the lack of significantly sized level-annotated data. ...
In our experiments, the best performing model for readability on learner texts achieves an accuracy of 0.797 and PCC of 0.938. ...
Acknowledgements We thank Cambridge Assessment for their assistance in the collection of the language testing data. ...
doi:10.18653/v1/w16-0502
dblp:conf/bea/XiaKB16
fatcat:wok7zumrbfgwbmp2srkua4aszy
THU QUANTA at TAC 2008 QA and RTE Track
2008
Text Analysis Conference
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This paper describes the systems of THU QUANTA in Text Analysis Conference (TAC) 2008. We participated in the Question Answering (QA) track, and the Recognizing Textual Entailment (RTE) track. ...
We detect the exact entity and relation mismatch to recognize the false entailment. The evaluation results show that the proposed approaches are very effective for the QA and RTE tasks. ...
If the time and date mismatch, we predict it is false entailment.
3) Location Mismatch For each location entity in H, if there is no corresponding entity in T, we predict it is false entailment. ...
dblp:conf/tac/LiZTBGZZH08
fatcat:4jrpex3urnaflnqqpbfvv55ojm
Back to the Past
2015
Proceedings of the Eighth ACM International Conference on Web Search and Data Mining - WSDM '15
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For this purpose, we propose (1) different query formulation methods for retrieving contextualization candidates and (2) ranking methods taking into account topical and temporal relevance as well as complementarity ...
In this paper, we present an approach for time-aware recontextualization, which takes those requirements into account in order to improve reading experience. ...
Prediction Performances The query formulation method described in Section 4.3 is based on predicting the performances (recall in our case) of candidate queries, ranking them according to the prediction ...
doi:10.1145/2684822.2685315
dblp:conf/wsdm/TranCKN15
fatcat:os6vzcuairc3deza6d6wbhpayq
PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity
[article]
2021
arXiv
pre-print
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In general, popular news usually contain important information and can attract users with different interests. Besides, they are usually diverse in content and topic. ...
However, these methods usually have difficulties in making accurate recommendations to cold-start users, and tend to recommend similar news with those users have read. ...
News Ranking and Model Training In this section, we introduce how we rank the candidate news and train the model in detail. ...
arXiv:2106.01300v2
fatcat:cg6xzv3y4rdhzcjcclv4efgaaq
Complex Knowledge Base Question Answering: A Survey
[article]
2022
arXiv
pre-print
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In detail, we begin with introducing the complex KBQA task and relevant background. ...
Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. ...
In the entity ranking paradigm, the entities contained in G q are candidates for answer prediction Ãq . ...
arXiv:2108.06688v2
fatcat:frcdrrhbsncm3kprehnz563yfq
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