A linguistic analysis of question taxonomies (original) (raw)
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Question terminologyis a set of terms which appear in keywords, idioms and fixed expressions commonly observed in questions. This paper investigates ways to automatically extract question terminology from a corpus of questions and represent them for the purpose of classifying byquestion type. Our key interest is to see whether or not semantic features can enhance the representation of strongly lexical nature of question sentences. We compare two feature sets: one with lexical features only, and another with a mixture of lexical and semantic features. For evaluation, we measure the classification accuracy made by two machine learning algorithms, C5.0 and PEBLS, by using a procedure calleddomain cross-validation, which effectively measures thedomain transferabilityof features.
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We have developed a method for answering single answer questions automatically using a collection of documents or the Internet as a source of data for the production of the answer. Examples of such questions are 'What is the melting point of tin?', and 'Who wrote the novel Moby Dick?'. The approach we have adopted to the problem uses the Mikrokosmos ontology to represent knowledge about question and answer content. A specialized lexicon of English connects words, in English, to their ontological meanings. Analysis of texts (both questions and documents) is based on a statistical part-of speech tagger, and pattern-based proper name and fact classification and phrase recognition. The system assumes that all the information required to produce an answer exists in a single sentence and retrieval strategies (where possible) are geared to finding documents in which this is the case. In this paper we describe the overall structure of the system and the operation of the various components.
Machine Learning based Question Classification
The Question Answering Systems (QASs) use method of information retrieval and Information extraction to retrieves documents that contain special answers to the question. One of the existence problems is finding the desired information from this very high variety. For this reason, it is necessary to find ways for organizing, classification and retrieving of information. Question classification plays an important role in providing a correct answer on QASs because giving a bunch of formulated questions to provide the correct answer from among the many documents will be highly effective. The aim of classification is selecting suitable label for questions based on the expected response. In this paper, we investigate the effect of automatically classifying questions on machine learning algorithms. In this paper, we will explain different types of algorithms and compare and evaluate them and next we will investigate the existence algorithms' weakness and advantage in question classification. As a result, in the past most classification was done based on sets of words that many studies show that to maximize the efficiency of the classification of algorithms we require semantics and in the questions we should looking for feature that be close to the meaning of questions. A great deal of research proposed to analysis and to classify emotions and to extract knowledge from them and to classify them using semantic and linguistic knowledge but it still requires a lot of research and development. 265 academic. Participating systems perform the same queries and retrieve relevant documents. Results are evaluated manually in a separate QAS and the assessment is carried in the so-called separate QAS. The Text Retrieval Conference has started with this purpose in [1], [4]. If search engines were able to receive the user ' question in the form of a question in natural language, understood with minimum redundancy and maximum precision, and respond to the massive volume of retrieved documents, we were not faced with the problems in the retrieval systems. In this regard question classification, which is categorized by putting questions in a sense, plays a key role in this system [3], [4].
Why-type Question Classification in Question Answering System
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The fundamental requisite to acquire information on any topic has become increasingly important. The need for Question Answering Systems (QAS) prevalent nowadays, replacing the traditional search engines stems from the user requirement for the most accurate answer to any question or query. Thus, interpreting the information need of the users is quite crucial for designing and developing a question answering system. Question classification is an important component in question answering systems that helps to determine the type of question and its corresponding type of answer. In this paper, we present a new way of classifying Why-type questions, aimed at understanding a questioner’s intent. Our taxonomy classifies Why-type questions into four separate categories. In addition, to automatically detect the categories of these questions by a parser, we differentiate them at lexical level.
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Question-answering has become one of the most popular information retrieval applications. Despite that most questionanswering systems try to improve the user experience and the technology used in finding relevant results, many difficulties are still faced because of the continuous increase in the amount of web content. Questions Classification (QC) plays an important role in question-answering systems, with one of the major tasks in the enhancement of the classification process being the identification of questions types. A broad range of QC approaches has been proposed with the aim of helping to find a solution for the classification problems; most of these are approaches based on bag-of-words or dictionaries. In this research, we present an analysis of the different type of questions based on their grammatical structure. We identify different patterns and use machine learning algorithms to classify them. A framework is proposed for question classification using a grammar-based approach (GQCC) which exploits the structure of the questions. Our findings indicate that using syntactic categories related to different domain-specific types of Common Nouns, Numeral Numbers and Proper Nouns enable the machine learning algorithms to better differentiate between different question types. The paper presents a wide range of experiments the results show that the GQCC using J48 classifier has outperformed other classification methods with 90.1% accuracy.
A Multilingual Approach to Question Classification
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 2018
In this paper we present the Konstanz Resource of Questions (KRoQ), the first dependency-parsed, parallel multilingual corpus of information-seeking and non-information-seeking questions. In creating the corpus, we employ a linguistically motivated rule-based system that uses linguistic cues from one language to help classify and annotate questions across other languages. Our current corpus includes German, French, Spanish and Koine Greek. Based on the linguistically motivated heuristics we identify, a two-step scoring mechanism assigns intra-and inter-language scores to each question. Based on these scores, each question is classified as being either information seeking or non-information seeking. An evaluation shows that this mechanism correctly classifies questions in 79% of the cases. We release our corpus as a basis for further work in the area of question classification. It can be utilized as training and testing data for machine-learning algorithms, as corpus-data for theoretical linguistic questions or as a resource for further rule-based approaches to question identification.
Analysis of Why-Type Questions for the Question Answering System
Communications in Computer and Information Science, 2018
The fundamental requisite to acquire information on any topic has become increasingly important. The need for Question Answering Systems (QAS) prevalent nowadays, replacing the traditional search engines stems from the user requirement for the most accurate answer to any question or query. Thus, interpreting the information need of the users is quite crucial for designing and developing a question answering system. Question classification is an important component in question answering systems that helps to determine the type of question and its corresponding type of answer. In this paper, we present a new way of classifying Why-type questions, aimed at understanding a questioner's intent. Our taxonomy classifies Why-type questions into four separate categories. In addition, to automatically detect the categories of these questions by a parser, we differentiate them at lexical level.
Analysis of statistical question classification for fact-based questions
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Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific.