دانلود مقاله ISI انگلیسی شماره 79649
عنوان فارسی مقاله

یادگیری ویژگی مبتنی بر برنامه نویسی ژنتیکی برای پاسخ دادن درخواست

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79649 2016 18 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Genetic programming-based feature learning for question answering
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Information Processing & Management, Volume 52, Issue 2, March 2016, Pages 340–357

کلمات کلیدی
پاسخ به سوال (QA)؛ آموزش ویژگی؛ برنامه نویسی ژنتیک (GP) الگوریتم؛ یادگیری وزن ویژگی های؛ پرسش حقایق؛ استخراج اطلاعات (IE)
پیش نمایش مقاله
پیش نمایش مقاله یادگیری ویژگی مبتنی بر برنامه نویسی ژنتیکی برای پاسخ دادن درخواست

چکیده انگلیسی

Question Answering (QA) systems are developed to answer human questions. In this paper, we have proposed a framework for answering definitional and factoid questions, enriched by machine learning and evolutionary methods and integrated in a web-based QA system. Our main purpose is to build new features by combining state-of-the-art features with arithmetic operators. To accomplish this goal, we have presented a Genetic Programming (GP)-based approach. The exact GP duty is to find the most promising formulas, made by a set of features and operators, which can accurately rank paragraphs, sentences, and words. We have also developed a QA system in order to test the new features. The input of our system is texts of documents retrieved by a search engine. To answer definitional questions, our system performs paragraph ranking and returns the most related paragraph. Moreover, in order to answer factoid questions, the system evaluates sentences of the filtered paragraphs ranked by the previous module of our framework. After this phase, the system extracts one or more words from the ranked sentences based on a set of hand-made patterns and ranks them to find the final answer. We have used Text Retrieval Conference (TREC) QA track questions, web data, and AQUAINT and AQUAINT-2 datasets for training and testing our system. Results show that the learned features can perform a better ranking in comparison with other evaluation formulas.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.