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A framework for merging and ranking of answers in DeepQA

D. C. Gondek, A. Lally, A. Kalyanpur, J. W. Murdock, P. A. Duboue, L. Zhang, Y. Pan, Z. M. Qiu, C. Welty
2012 IBM Journal of Research and Development  
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]

Mohammed ElKomy, Amany M. Sarhan
2022 arXiv   pre-print
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

Mourad Sarrouti, Said Ouatik El Alaoui
2017 BioNLP 2017  
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]

Abeer El-korany
2013 arXiv   pre-print
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

Abeer El-korany
2013 International journal of Web & Semantic Technology  
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

Shaurya Rohatgi, Jian Wu, C. Lee Giles
2020 Conference and Labs of the Evaluation Forum  
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

Ibrahim Burak Özyurt
2021 Conference and Labs of the Evaluation Forum  
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

Georgios Balikas, Aris Kosmopoulos, Anastasia Krithara, Georgios Paliouras, Ioannis A. Kakadiaris
2015 Conference and Labs of the Evaluation Forum  
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]

Anastasios Nentidis, Konstantinos Bougiatiotis, Anastasia Krithara, Georgios Paliouras
2020 Communications in Computer and Information Science  
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]

Anastasios Nentidis, Georgios Katsimpras, Eirini Vandorou, Anastasia Krithara, Luis Gasco, Martin Krallinger, Georgios Paliouras
2021 arXiv   pre-print
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]

Anastasios Nentidis, Anastasia Krithara, Konstantinos Bougiatiotis, Martin Krallinger, Carlos Rodriguez-Penagos, Marta Villegas, Georgios Paliouras
2020 Lecture Notes in Computer Science  
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

Manvi Breja, Sanjay Kumar Jain
2022 Journal of Cases on Information Technology  
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

Mai-Vu Tran, Duc-Trong Le, Xuan-Tu Tran, Tien-Tung Nguyen
2012 Pacific Asia Conference on Language, Information and Computation  
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]

Elif Aktolga, James Allan, David A. Smith
2011 Lecture Notes in Computer Science  
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]

Jamshid Mozafari, Mohammad Ali Nematbakhsh, Afsaneh Fatemi
2019 arXiv   pre-print
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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