Machine Learning based Question Classification (original) (raw)
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Machine Learning based Question Classification Methods in the Question Answering Systems
International Journal of Innovation and Applied Studies, 2013
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].
A REVIEW ON QUESTION CLASSIFICATION USING MACHINE LEARNING BASED ON SEMANTIC FEATURES
One of the most important aspects of the learning process is the assessment of the knowledge acquired by the learners. In typical assessment like Exam, Assignment or Quiz, a grader provides students with feedback on their answers to questions related to the subject matter. In this way Question classification plays an important role in Question answering. Its main role is to assign a suitable semantic category to the question posted in natural language that represents the type of the required answers. It is a major challenge for the automated Question Classification function. In order to solve this kind of complexity, learners use lexical, syntactic and semantic features to analyze the questions. This paper presents a review on various approaches for Question classification using Machine learning approach based on Semantic Feature.
A survey on question answering systems with classification
Question answering systems (QASs) generate answers of questions asked in natural languages. Early QASs were developed for restricted domains and have limited capabilities. Current QASs focus on types of questions generally asked by users, characteristics of data sources consulted, and forms of correct answers generated. Research in the area of QASs began in 1960s and since then, a large number of QASs have been developed. To identify the future scope of research in this area, the need of a comprehensive survey on QASs arises naturally. This paper surveys QASs and classifies them based on different criteria. We identify the current status of the research in the each category of QASs, and suggest future scope of the research.
Question classification using support vector machine and pattern matching
Journal of theoretical and applied information technology, 2016
Question classification plays a crucial role in the question answering system, and it aim to accurately assign one or more labels to question based on expected answer type. Nonetheless, classifying user's question is a very challenging task due to the flexibility of Natural Language where a question can be written in many different forms and information within the sentence may not be enough to effectively to classify the question. Limited researches have focused on question classification for Arabic question answering. In this research we used support vector machine (SVM) and pattern matching to classify question into three main classes which are "Who", "Where" and "What". The SVM leverage features such as n-gram and WordNet. The WordNet is used to map words in questions to their synonyms that have the same meaning. Five pattern were introduced to analyze "What" question and label the questions with "definition", "person&quo...
Question classification using support vector machines
2003
Question classification is very important for question answering. This paper presents our research work on automatic question classification through machine learning approaches. We have experimented with five machine learning algorithms: Nearest Neighbors (NN), Naïve Bayes (NB), Decision Tree (DT), Sparse Network of Winnows (SNoW), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-ofngrams. The experiment results show that with only surface text features the SVM outperforms the other four methods for this task. Further, we propose to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions. We describe how the tree kernel can be computed efficiently by dynamic programming. The performance of our approach is promising, when tested on the questions from the TREC QA track.
International Journal of Computer Applications
Search engines have played a very important role in helping the users to search the necessary information from the huge information. By displaying the list of links to documents.The Question-Answering systems are gaining popularity. Because The main benefit of such QA systems is that the user can ask the query (question) in natural language and he /she get a precise and appropriate answer instead of just displaying a list of links to documents. The main advantage of the proposed Question answering system, which is not restricted to a specific domain. This approach is related to a natural language interface to the database (NLIDB), which takes a natural language query as input and giving the appropriate answer from the manually created knowledge base(structured database). There are two main steps of implementation of the proposed question answering system. The first step is to use a classifier to identify appropriate tables and columns in a structured database for an incoming question, and the second step is to perform the free text retrieval to lookup answer. The system uses named entity normalization, part-of-speech tagging, and a statistical classifier trained on data from the TREC QA task.
An Empirical Comparison of Question Classification Methods for Question Answering Systems
2020
Question classification is an important component of Question Answering Systems responsible for identifying the type of an answer a particular question requires. For instance, “Who is the prime minister of the United Kingdom?” demands a name of a PERSON, while “When was the queen of the United Kingdom born?” entails a DATE. This work makes an extensible review of the most recent methods for Question Classification, taking into consideration their applicability in low-resourced languages. First, we propose a manual classification of the current state-of-the-art methods in four distinct categories: low, medium, high, and very high level of dependency on external resources. Second, we applied this categorization in an empirical comparison in terms of the amount of data necessary for training and performance in different languages. In addition to complementing earlier works in this field, our study shows a boost on methods relying on recent language models, overcoming methods not suitab...
The Question Answering Systems: A Survey
Question Answering (QA) is a specialized area in the field of Information Retrieval (IR). The QA systems are concerned with providing relevant answers in response to questions proposed in natural language. QA is therefore composed of three distinct modules, each of which has a core component beside other supplementary components. These three core components are: question classification, information retrieval, and answer extraction. Question classification plays an essential role in QA systems by classifying the submitted question according to its type. Information retrieval is very important for question answering, because if no correct answers are present in a document, no further processing could be carried out to find an answer. Finally, answer extraction aims to retrieve the answer for a question asked by the user. This survey paper provides an overview of Question-Answering and its system architecture, as well as the previous related work comparing each research against the others with respect to the components that were covered and the approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along with their main contributions, experimental results, and limitations.
2002
Abstract 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.