Question classification using head words and their hypernyms

Huang, Zhiheng; Thint, Marcus; Qin, Zengchang

doi:10.3115/1613715.1613835

Cited by 140 publications

(143 citation statements)

References 18 publications

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“…The head word feature is widely used for question classification in [16], [20]. We access the performance of our methods on different categories of questions.…”

Section: Data Preparationmentioning

confidence: 99%

Expert Finding for Question Answering via Graph Regularized Matrix Completion

Zhao

Zhang

et al. 2015

IEEE Trans. Knowl. Data Eng.

152

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Abstract-Expert finding for question answering is a challenging problem in community-based question answering (CQA) systems, arising in many real applications such as question routing and identification of best answers. In order to provide high-quality experts, many existing approaches learn the user model from their past question-answering activities in CQA systems. However, the past activities of users in most CQA systems are rather few, and thus the user model may not be well inferred in practice. In this paper, we consider the problem of expert finding from the viewpoint of missing value estimation. We then employ users' social networks for inferring user model, and thus improve the performance of expert finding in CQA systems. In addition, we develop a novel graph-regularized matrix completion algorithm for inferring the user model. We further develop two efficient iterative procedures, GRMC-EGM and GRMC-AGM, to solve the optimization problem. GRMC-EGM utilizes the Extended Gradient Method (EGM), while GRMC-AGM applies the Accelerated proximal Gradient search Method (AGM), for the optimization. We evaluate our methods on the well-known question answering system Quora, and the popular social network Twitter. Our empirical study shows the effectiveness of the proposed algorithms in comparison to the state-of-the-art expert finding algorithms.

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“…The head word feature is widely used for question classification in [16], [20]. We access the performance of our methods on different categories of questions.…”

Section: Data Preparationmentioning

confidence: 99%

Expert Finding for Question Answering via Graph Regularized Matrix Completion

Zhao

Zhang

et al. 2015

IEEE Trans. Knowl. Data Eng.

152

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“…Our system used the Stanford Parser [5] because of its speed and reliability. The last noun of each noun phrase is selected as headword [6] of the sentence. Usually, they are the topics the user is talking about.…”

Section: Identifying Abstractmentioning

confidence: 99%

Confucius computer: a philosophical digital agent for intergenerational philosophical play

Cheok

Edirisinghe

Karunanayaka

2016

Pers Ubiquit Comput

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Confucianism is commonly defined as ''... a system of philosophical, ethical and political thought based on the teachings of Confucius,'' which originated through the teachings of Confucius during the sixth-century BCE. It is a way of life or a philosophy of human nature that considers human relationships as the foundation of the society. Confucius teachings had highly influenced the development of several cultures in Asia, making Confucianism an intangible cultural heritage. In this paper, we are re-acquainting users with an intangible heritage that is part of their everyday, by developing a system that permits experiencing Confucius teachings virtually and interactively. The system can measure philosophical intent of the human and generate meaningful philosophical answers. It is also aimed for intergenerational sharing of Confucius heritage through a simple interactive process with the virtual sage making the experience enjoyable and entertaining. Previous research in natural language processing (NLP) mainly focused on the understanding and delivering of human natural language accurately. In this research, we explored how to apply NLP to model the knowledge and teachings of Confucius, through the natural conversation between human and computer. This virtual Confucius, a chat agent that generates outputs based on Confucius teachings, using a series of algorithms and techniques to improve the matching accuracy between user input and computer output, introduces a novel way of interacting with intangible cultures. Our user evaluation results revealed that there is a positive correlation between relevance and enjoyment, finding their experiences interacting with virtual Confucius very encouraging. Adults who participated in experiencing the virtual Confucius together with their children believed that this system has the potential to improve intergenerational interactions through shared play.

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“…Huang et al [20] introduced 5 categories for word shapes: all digit, lower case, upper case, mixed and other.…”

Section: Word Shapesmentioning

confidence: 99%

“…For instance, in DESCRIPTION:DEFINITION questions such as What is a bird?,the headword bird is futile because the question is asking for a definition. To prevent some of these pitfalls, we compile a small set of patterns (some of which are adapted from [20], so that when a question matches one of the patterns, a placeholder is returned instead of the question headword. …”

Section: Question Patternsmentioning

confidence: 99%

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Question Classification using Semantic, Syntactic and Lexical features

Mishra¹,

Mishra²,

Sharma³

2013

IJWesT

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Question classification is very important for question answering. This paper present our research work on question classification through machine learning approach. In order to train the learning model, we designed a rich set of features that are predictive of question categories. An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a question. In this work we combined lexical, syntactic and semantic features which improve the accuracy of classification. Furthermore, we adopted three different classifiers: Nearest Neighbors (NN), Naïve Bayes (NB), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-of n grams.Furthermore, we discovered that when we take SVM classifier and combine the semantic, syntactic, lexical feature we found that it will improve the accuracy of classification. We tested our proposed approaches on the well-known UIUC dataset and succeeded to achieve a new record on the accuracy of classification on this dataset.

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Question classification using head words and their hypernyms

Cited by 140 publications

References 18 publications

Expert Finding for Question Answering via Graph Regularized Matrix Completion

Expert Finding for Question Answering via Graph Regularized Matrix Completion

Confucius computer: a philosophical digital agent for intergenerational philosophical play

Question Classification using Semantic, Syntactic and Lexical features

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