Learning to rank for why-question answering
In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why -questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among w...
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| Published in: | Information retrieval (Boston) Vol. 14; no. 2; pp. 107 - 132 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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| Abstract | In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to
why
-questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and
SVM
map
) in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on learning to rank for Information Retrieval. |
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| AbstractList | In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and SVM super( )map in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on learning to rank for Information Retrieval. In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and SVM map ) in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on learning to rank for Information Retrieval. In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why -questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and SVM map ) in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on learning to rank for Information Retrieval. In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and SVM ^sup map^) in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on learning to rank for Information Retrieval.[PUBLICATION ABSTRACT] |
| Author | Theijssen, Daphne Boves, Lou Raaijmakers, Stephan Verberne, Suzan van Halteren, Hans |
| Author_xml | – sequence: 1 givenname: Suzan surname: Verberne fullname: Verberne, Suzan email: s.verberne@let.ru.nl organization: Centre for Language and Speech Technology, Radboud University – sequence: 2 givenname: Hans surname: van Halteren fullname: van Halteren, Hans organization: Centre for Language and Speech Technology, Radboud University – sequence: 3 givenname: Daphne surname: Theijssen fullname: Theijssen, Daphne organization: Department of Linguistics, Radboud University – sequence: 4 givenname: Stephan surname: Raaijmakers fullname: Raaijmakers, Stephan organization: TNO Information and Communication Technology – sequence: 5 givenname: Lou surname: Boves fullname: Boves, Lou organization: Centre for Language and Speech Technology, Radboud University |
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| Cites_doi | 10.1145/1147197.1147210 10.3115/1599081.1599201 10.1007/s10994-005-0918-9 10.1145/1390156.1390306 10.1561/1500000016 10.1023/A:1022602019183 10.1109/TSMCB.2008.2002909 10.1002/asi.20009 10.1145/1277741.1277809 10.3233/IDA-2002-6504 10.1162/jmlr.2003.4.6.933 10.1162/coli.09-032-R1-08-034 10.3115/1289189.1289206 10.1145/1102351.1102363 10.21236/ADA456400 10.1145/775047.775067 10.1007/11776420_44 |
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| Keywords | questions Learning to rank Question answering Why-questions Information retrieval Query processing Machine learning |
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| Snippet | In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to
why
-questions. We use TF-IDF together with a set of 36... In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36... |
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| SubjectTerms | Artificial intelligence Computer Science Data Mining and Knowledge Discovery Data Structures and Information Theory Datasets Exact sciences and technology Experiments Genetic algorithms Information and communication sciences Information processing and retrieval Information retrieval Information retrieval. Man machine relationship Information science. Documentation Information Storage and Retrieval Learning Machine learning Natural Language Processing (NLP) Pattern Recognition Questions Ranking Ranking systems Ratings & rankings Regression Regression analysis Relevance Research process. Evaluation Sciences and techniques of general use Studies Support vector machines Tasks Tuning |
| Title | Learning to rank for why-question answering |
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