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A framework for merging and ranking of answers in DeepQA
2012
IBM Journal of Research and Development
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In DeepQA, this is done using a machine learning framework that is phase-based, providing capabilities for manipulating the data and applying machine learning in successive applications. ...
The final stage in the IBM DeepQA pipeline involves ranking all candidate answers according to their evidence scores and judging the likelihood that each candidate answer is correct. ...
Acknowledgments The authors would like to acknowledge the contributions of other researchers who have done important work involving merging and ranking answers in DeepQA, including Apoorv Agarwal, Eric ...
doi:10.1147/jrd.2012.2188760
fatcat:6bsqnvvepvchza75qj5bhhdyhu
TCE at Qur'an QA 2022: Arabic Language Question Answering Over Holy Qur'an Using a Post-Processed Ensemble of BERT-based Models
[article]
2022
arXiv
pre-print
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Question answering is one of these tasks which is used by search engines and social media platforms for improved user experience. ...
In this article, we describe our attempts at OSACT5 Qur'an QA 2022 Shared Task, which is a question answering challenge on the Holy Qur'an in Arabic. ...
., Mohamed, A., Farouk, B., El-Makky, N., and Torki, M. (2014). Al-bayan: An Arabic question answering system for the holy quran. In Proceedings of the EMNLP 2014 ...
arXiv:2206.01550v1
fatcat:g47gcu7tozczxfd3eakq3pleqe
A Biomedical Question Answering System in BioASQ 2017
2017
BioNLP 2017
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and term frequency metric for extracting the exact answers of factoid and list questions, and (3) the BM25 model and UMLS concepts for retrieving the ideal answers (i.e., paragraph-sized summaries). ...
Our system, dealing with four types of questions (i.e., yes/no, factoid, list, and summary), is based on (1) a dictionary-based approach for generating the exact answers of yes/no questions, (2) UMLS metathesaurus ...
They have built a generic retrieval model based on the sequential dependence model, word embedding and ranking model for document retrieval. ...
doi:10.18653/v1/w17-2337
dblp:conf/bionlp/SarroutiA17
fatcat:n3qa2pvzhrb6tcsdokyicuqlpq
Integrated Expert Recommendation Model for Online Communities
[article]
2013
arXiv
pre-print
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Vector space model is used to compute the relevance of published content with respect to a specific query while PageRank algorithm is applied to rank candidate experts. ...
This paper proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from enormous contents and social network features. ...
Therefore, top ranked 20 candidate experts are used as a nominated list for the next phase. ...
arXiv:1311.3394v1
fatcat:enl7pgh7urftnbq2ylkwpfkh7i
Integrated Expert Recommendation Model for Online Communities
2013
International journal of Web & Semantic Technology
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Vector space model is used to compute the relevance of published content with respect to a specific query while PageRank algorithm is applied to rank candidate experts. ...
This paper proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from enormous contents and social network features. ...
Therefore, top ranked 20 candidate experts are used as a nominated list for the next phase. ...
doi:10.5121/ijwest.2013.4402
fatcat:a4kdhsqka5bejca4e4jxvkjrbu
PSU at CLEF-2020 ARQMath Track: Unsupervised Re-ranking using Pretraining
2020
Conference and Labs of the Evaluation Forum
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For the re-ranking we use a pre-trained robertabase model (110 million parameters) to make the language model more math-aware. ...
Our primary run for the main Task-1: Question Answering uses a two-stage retrieval technique in which the first stage is a fusion of traditional BM25 scoring and tf-idf with cosine similarity-based retrieval ...
Special thanks to Behrooz Mansouri for providing the dataset, initial analysis of topics, and starter code to all the participants of the task; it made it easier for us to pre-process the data and jump ...
dblp:conf/clef/Rohatgi0G20
fatcat:76djkwmfejbeva5e7j2y74irve
End-to-end Biomedical Question Answering via Bio-AnswerFinder and Discriminative Language Representation Models
2021
Conference and Labs of the Evaluation Forum
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Generative Transformers based language representation models such as BERT and its biomedical domain adapted version BioBERT have been shown to be highly effective for biomedical question answering. ...
The introduced language representation models outperformed other language models including BioBERT in answer span classification, answer candidate re-ranking and yes/no answer classification tasks. ...
I would like also to thank Google TensorFlow Research Cloud (TFRC) program for providing me with free TPUs which allowed me to pretrain Bio-ELECTRA models. ...
dblp:conf/clef/Ozyurt21
fatcat:xcjfp4mjrzbwnf26x2265ucbge
Results of the BioASQ Tasks of the Question Answering Lab at CLEF 2015
2015
Conference and Labs of the Evaluation Forum
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The question answering task was further subdivided into two phases. 24 systems from 9 different teams participates in the annotation phase (Task 3b-phase A), while 26 systems of 10 different teams participated ...
in the answer generation phase (Task 3b-phase B). ...
Acknowledgments The third edition of BioASQ is supported by a conference grant from the NIH/NLM (number 1R13LM012214-01) and sponsored by the companies Viseo and Atypon. ...
dblp:conf/clef/BalikasKKPK15
fatcat:7fctnikrong7pn53m7tahpr5n4
Results of the Seventh Edition of the BioASQ Challenge
[chapter]
2020
Communications in Computer and Information Science
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2020 pre-print arXiv:2006.09174v1 fatcat:xr7danogirdznbw7obb3ygg3h4
The aim of the BioASQ challenge is the promotion of systems and methodologies through the organization of a challenge on the tasks of large-scale biomedical semantic indexing and question answering. ...
Acknowledgments Google was a proud sponsor of the BioASQ Challenge in 2018. The seventh edition of BioASQ is also sponsored by the Atypon Systems inc. ...
BioASQ is grateful to NLM for providing baselines for task 7a and to the CMU team for providing the baselines for task 7b. Finally, we would also like to thank all teams for their participation. ...
doi:10.1007/978-3-030-43887-6_51
fatcat:76ind5ijizdphjmcnzgk7fjmii
Overview of BioASQ 2021: The ninth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
[article]
2021
arXiv
pre-print
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In this year, a new question answering task, named Synergy, is introduced to support researchers studying the COVID-19 disease and measure the ability of the participating teams to discern information ...
Advancing the state-of-the-art in large-scale biomedical semantic indexing and question answering is the main focus of the BioASQ challenge. ...
Task 9b is divided into two phases: (phase A) the retrieval of the required information and (phase B) answering the question. ...
arXiv:2106.14885v1
fatcat:uix2mc6jh5hg3jegwzfhwblzc4
Overview of BioASQ 2020: The Eighth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
[chapter]
2020
Lecture Notes in Computer Science
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2021 pre-print arXiv:2106.14618v1 fatcat:2b3jafadjrd6hk4v3r6zv7t5qy
BioASQ is a series of challenges aiming at the promotion of systems and methodologies for large-scale biomedical semantic indexing and question answering. ...
This year, the challenge has been extended with the introduction of a new task on medical semantic indexing in Spanish. ...
For the second model, they regard the sentences of the provided snippets as candidate ideal answers and build a ranking model with two parts. ...
doi:10.1007/978-3-030-58219-7_16
fatcat:wekghiprdzc53ejvwxdvn5d2xq
Analyzing Linguistic Features for Answer Re-Ranking of Why-Questions
2022
Journal of Cases on Information Technology
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candidate being relevant for the question. ...
An answer re-ranker model is implemented that finds the highest ranked answer comprising largest value of feature similarity between question and answer candidate and thus achieving 0.64 Mean Reciprocal ...
Solution: The paper has utilized the concept of answer type matching for returning one appropriate answer to a question during answer validation phase. ...
doi:10.4018/jcit.20220701.oa10
fatcat:rd2wqn3p2ndadnh4qhqqmmta2i
A Model of Vietnamese Person Named Entity Question Answering System
2012
Pacific Asia Conference on Language, Information and Computation
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In this paper, we proposed a Vietnamese named entity question answering (QA) model. ...
We gathered a Vietnamese question dataset containing about 2000 popular "Who, Whom, Whose" questions to evaluate our question chunking method and QA model. ...
In the initial phase, questions are parsed by using CRF model. ...
dblp:conf/paclic/TranLTN12
fatcat:jjzlz63p4vgl7nhn7nsuyuhzri
Passage Reranking for Question Answering Using Syntactic Structures and Answer Types
[chapter]
2011
Lecture Notes in Computer Science
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Passage Retrieval is a crucial step in question answering systems, one that has been well researched in the past. ...
Whereas in previous work, passages are reranked only on the basis of syntactic structures of questions and answers, our method achieves a better ranking by aligning the syntactic structures based on the ...
This work was supported in part by the Center for Intelligent Information Retrieval. ...
doi:10.1007/978-3-642-20161-5_62
fatcat:az6iraob2nggfhgtvcflpqchya
Attention-based Pairwise Multi-Perspective Convolutional Neural Network for Answer Selection in Question Answering
[article]
2019
arXiv
pre-print
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In this research, a model is proposed to select the most relevant answers to the factoid question from the candidate answers. ...
The proposed model ranks the candidate answers in terms of semantic and syntactic similarity to the question, using convolutional neural networks. ...
Finally, the calculated similarity for the positive answer-question and the negative answer-question are ranked using the pairwise ranking. ...
arXiv:1909.01059v3
fatcat:gv2i7hgzpnfldd6zqb3sztppt4
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