Questions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange. The surface form of a question can signal the intention and background of the person asking it, as well as the nature of their relation with the interlocutor. While the informational nature of questions has been extensively examined in the context of question-answering applications, their rhetorical aspects have been largely understudied. In this work we introduce an unsupervised methodology for extracting surface motifs that recur in questions, and for grouping them according to their latent rhetorical role. By applying this framework to the setting of question sessions in the UK parliament, we show that the resulting typology encodes key aspects of the political discourse---such as the bifurcation in questioning behavior between government and opposition parties---and reveals new insights into the effects of a legislator's tenure and political career ambitions.
Question-answering Computationally, questions have received considerable attention in the context of question-answering (QA) systems---for a survey see ---with an emphasis on understanding their information need @cite_25 . Techniques have been developed to categorize questions based on the nature of these information needs in the context of the TREC QA challenge @cite_3 , and to identify questions asking for similar information @cite_39 @cite_35 @cite_17 ; questions have also been classified by topic @cite_24 and quality @cite_27 @cite_5 . In contrast, our work is not concerned with the information need central to QA applications, and instead focuses on the rhetorical aspect of questions.
@cite_35 @cite_3 @cite_39 @cite_24 @cite_27 @cite_5 @cite_25 @cite_17
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Question Answering (QA) is retrieving answers to natural language questions from a collection of documents rather than retrieving relevant documents containing the keywords of the query which is performed by search engines. What a user usually wants is often a precise answer to a question. For example, given the question “Who won the nobel prize in peace in 2006?” what a user really wants is the answer “Dr. Muhammad Yunus”, in stead of reading through lots of documents that contain the words “win”, “nobel”,“prize”, “peace” and “2006” etc. This means that question answering systems will possibly be integral to the next generation of search engines. The Text Retrieval Conference, TREC1 QA track is the major large-scale evaluation environment for open-domain question answering systems. The questions in the TREC-2007 QA track are clustered by target, which is the overall theme or topic of the questions. The track has three types of questions: 1. factoid that require only one correct response, 2. list that require a non redundant list of correct responses and 3. other questions that require a non redundant list of facts about the target that has not already been discovered by a previous answer. We took the approach of designing a question answering system that is based on document tagging and question classification. Question classification extracts useful information (i.e. answer type) from the question about how to answer the question. Document tagging extracts useful information from the documents, which will be used in finding the answer to the question. We used different available tools to tag the documents. Our system classifies the questions using manually developed rules. In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer.This paper presents a machine learning approach to question classification. We learn a hierarchical classifier that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes. We show accurate results on a large collection of free-form questions used in TREC 10. To respond correctly to a free form factual question given a large collection of text data, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. This work presents a machine learning approach to question classification. Guided by a layered semantic hierarchy of answer types, we develop a hierarchical classifier that classifies questions into fine-grained classes. This work also performs a systematic study of the use of semantic information sources in natural language classification tasks. It is shown that, in the context of question classification, augmenting the input of the classifier with appropriate semantic category information results in significant improvements to classification accuracy. We show accurate results on a large collection of free-form questions used in TREC 10 and 11. Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval2015 Task 3 on Answer Selection in cQA, which included two subtasks: (a) classifying answers as good, bad, or potentially relevant with respect to the question, and (b) answering a YES NO question with yes, no, or unsure, based on the list of all answers. We set subtask A for Arabic and English on two relatively different cQA domains, i.e., the Qatar Living website for English, and a Quran-related website for Arabic. We used crowdsourcing on Amazon Mechanical Turk to label a large English training dataset, which we released to the research community. Thirteen teams participated in the challenge with a total of 61 submissions: 24 primary and 37 contrastive. The best systems achieved an official score (macro-averaged F1) of 57.19 and 63.7 for the English subtasks A and B, and 78.55 for the Arabic subtask A.