Twitter named entity recognition is the process of identifying proper names and classifying them into some predefined labels/categories. The paper introduces a Twitter named entity system using a supervised machine learning approach, namely Conditional Random Fields. A large set of different features was developed and the system was trained using these. The Twitter named entity task can be divi...

In the last decades, named entity recognition has been extensively studied with various supervised learning approaches depend on massive labeled data. In this paper, we focus on person name recognition in judgment documents. Owing to the lack of human-annotated data, we propose a joint learning approach, namely Aux-LSTM, to use a large scale of auto-annotated data to help human-annotated data (...

Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products. Many researchers have attacked this problem in a variety of languages but only a few limited researches have focused on Named Entity Recognition (NER) for Arabic text due to the lack of resources for Arabic named entities and the limited amount of progress made in Ar...

The popularity of social networks has made them a perfect medium for activity or advertising campaign promotion. Therefore, many people use Facebook pages to announce their advertising campaign. The purpose of this study is to extract activity events by constructing two named entity recognition models, namely activity name and location, via a Web NER model generation tool [1]. We enhance the to...

In this interactive presentation, a Chinese named entity and relation identification system is demonstrated. The domainspecific system has a three-stage pipeline architecture which includes word segmentation and part-of-speech (POS) tagging, named entity recognition, and named entity relation identitfication. The experimental results have shown that the average F-measure for word segmentation a...

This paper briefly describes our system in The Fourth SIGHAN Bakeoff. Discriminative models including maximum entropy model and conditional random fields are utilized in Chinese word segmentation and named entity recognition with different tag sets and features. Transformation-based learning model is used in part-of-speech tagging. Evaluation shows that our system achieves the F-scores: 92.64% ...

Recognizing and extracting exact name entities, like Persons, Locations and Organizations are very useful to mining information from text. Learning to extract names in natural language text is called Named Entity Recognition (NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and Summarization Systems, In...

The aim of this study is to build a tool for Part of Speech (POS) tagging and Name Entity Recognition for Arabic Language, the approach used to build this tool is a rule base technique. The POS Tagger contains two phases:The first phase is to pass word into a lexicon phase, the second level is the morphological phase, and the tagset are (Noun, Verb and Determine). The Named-Entity detector will...

Foreign name expressions written in Chinese characters are difficult to recognize since the sequence of characters represents the Chinese pronunciation of the name. This paper suggests that known English or German person names can reliably be identified on the basis of the similarity between the Chinese and the foreign pronunciation. In addition to locating a person name in the text and learnin...

Name Entity Recognition (NER) is an important process used for several type of applications such as Information Extraction, Information Retrieval, Question Answering, text clustering, etc. It is intended to identify and classify name entities from a given text. NER is performed by using a rule-based approach that relies on human intuitive or machine learning methods such as Hidden Markov Model ...