Natural Language Processing

List of Articles Natural Language Processing

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1 - DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System
Shima Mehrabi Seyed Abolghassem Mirroshandel Hamidreza Ahmadifar
10.7508/jist.2019.03.005

With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language pro More

With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language processing researchers. Today, with improvement in processing power and the development of computational tools, efforts to improve the performance of the summarization system is continued, especially with utilizing more powerful learning algorithms such as deep learning method. In this paper, a novel multi-lingual multi-document summarization system is proposed that works based on deep learning techniques, and it is amongst the first Persian summarization system by use of deep learning. The proposed system ranks the sentences based on some predefined features and by using a deep artificial neural network. A comprehensive study about the effect of different features was also done to achieve the best possible features combination. The performance of the proposed system is evaluated on the standard baseline datasets in Persian and English. The result of evaluations demonstrates the effectiveness and success of the proposed summarization system in both languages. It can be said that the proposed method has achieve the state of the art performance in Persian and English. Manuscript profile
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2 - A Customized Web Spider for Why-QA Pairs Corpus Preparation
Manvi Breja
10.52547/jist.15700.11.41.41

20.1001.1.23221437.2023.11.41.2.7

Considering the growth of researches on improving the performance of non-factoid question answering system, there is a need of an open-domain non-factoid dataset. There are some datasets available for non-factoid and even how-type questions but no appropriate dataset av More

Considering the growth of researches on improving the performance of non-factoid question answering system, there is a need of an open-domain non-factoid dataset. There are some datasets available for non-factoid and even how-type questions but no appropriate dataset available which comprises only open-domain why-type questions that can cover all range of questions format. Why-questions play a significant role and are usually asked in every domain. They are more complex and difficult to get automatically answered by the system as why-questions seek reasoning for the task involved. They are prevalent and asked in curiosity by real users and thus their answering depends on the users’ need, knowledge, context and their experience. The paper develops a customized web crawler for gathering a set of why-questions from five popular question answering websites viz. Answers.com, Yahoo! Answers, Suzan Verberne’s open-source dataset, Quora and Ask.com available on Web irrespective of any domain. Along with the questions, their category, document title and appropriate answer candidates are also maintained in the dataset. With this, distribution of why-questions according to their type and category are illustrated. To the best of our knowledge, it is the first large enough dataset of 2000 open-domain why-questions with their relevant answers that will further help in stimulating researches focusing to improve the performance of non-factoid type why-QAS. Manuscript profile
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3 - Designing a Semi-Intelligent Crawler for Creating a Persian Question Answering Corpus Called Popfa
Hadi Sharifian Nasim Tohidi Chitra Dadkhah

Question answering in natural language processing is an interesting field for researchers to examine their ability in solving the tough Alan Turing test. Every day computer scientists are trying hard to develop and promote question answering systems in various natural l More

Question answering in natural language processing is an interesting field for researchers to examine their ability in solving the tough Alan Turing test. Every day computer scientists are trying hard to develop and promote question answering systems in various natural languages, especially English. However, in Persian, it is not easy to advance these systems. The main problem is related to low resources and not enough corpora in this language. Thus, in this paper, a Persian question answering text corpus is created, which covers a wide range of religious, midwifery, and issues related to youth marriage topics and question types commonly encountered in Persian language usage. In this regard, the most important challenge was introducing a method for data gathering in Persian as well as facilitating and expanding the data gathering process. Though, SIC (Semi-Intelligent Crawler) is proposed as a solution that can overcome the challenge and find a way to crawl the Persian websites, gather text and finally import it to a database. The outcome of this research is a corpus called Popfa, which stands for POrsesh Pasokh (question answering) in FArsi. This corpus contains more than 53,000 standard questions and answers. Besides, it has been evaluated with standard approaches. All the questions in Popfa are answered by specialists in two general topics: religious and medical questions. Therefore, researchers can now use this corpus for doing research on Persian question answering. Manuscript profile

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List of Articles Natural Language Processing