Named Entity Relation Mining using Wikipedia
نویسندگان
چکیده
Discovering relations among Named Entities (NEs) from large corpora is both a challenging, as well as useful task in the domain of Natural Language Processing, with applications in Information Retrieval (IR), Summarization (SUM), Question Answering (QA) and Textual Entailment (TE). The work we present resulted from the attempt to solve practical issues we were confronted with while building systems for the tasks of Textual Entailment Recognition and Question Answering, respectively. The approach consists in applying grammar induced extraction patterns on a large corpus – Wikipedia – for the extraction of relations between a given Named Entity and other Named Entities. The results obtained are high in precision, determining a reliable and useful application of the built resource.
منابع مشابه
بهبود شناسایی موجودیت‌های نامدار فارسی با استفاده از کسره اضافه
Named entity recognition is a process in which the people’s names, name of places (cities, countries, seas, etc.) and organizations (public and private companies, international institutions, etc.), date, currency and percentages in a text are identified. Named entity recognition plays an important role in many NLP tasks such as semantic role labeling, question answering, summarization, machine ...
متن کاملRelation Extraction from Wikipedia Using Subtree Mining
The exponential growth and reliability of Wikipedia have made it a promising data source for intelligent systems. The first challenge of Wikipedia is to make the encyclopedia machine-processable. In this study, we address the problem of extracting relations among entities from Wikipedia’s English articles, which in turn can serve for intelligent systems to satisfy users’ information needs. Our ...
متن کاملSubtree Mining for Relation Extraction from Wikipedia
In this study, we address the problem of extracting relations between entities fromWikipedia’s English articles. Our proposed method first anchors the appearance of entities in Wikipedia’s articles using neither Named Entity Recognizer (NER) nor coreference resolution tool. It then classifies the relationships between entity pairs using SVM with features extracted from the web structure and sub...
متن کاملMining Transliterations from Wikipedia using Dynamic Bayesian Networks
Transliteration mining is aimed at building high quality multi-lingual named entity (NE) lexicons for improving performance in various Natural Language Processing (NLP) tasks including Machine Translation (MT) and Cross Language Information Retrieval (CLIR). In this paper, we apply two Dynamic Bayesian network (DBN)-based edit distance (ED) approaches in mining transliteration pairs from Wikipe...
متن کاملNamed Entity Recognition in Persian Text using Deep Learning
Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008