Hierarchical Contextualized Representation for Named Entity Recognition

Effective Word Representation for Named Entity Recognition

Recently, various machine learning models have been built using word-level embeddings and have achieved substantial improvement in NER prediction accuracy. Most NER models only take words as input and ignore character-level information. In this paper, we propose an effective word representation that efficiently includes both the word-level and character-level information by averaging its charac...

Named 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...

Hierarchical classification for Multilingual Language Identification and Named Entity Recognition

This paper describes the approach for Subtask-1 of the FIRE2015 Shared Task on Mixed Script Information Retrieval. The subtask involved multilingual language identification (including mixed words and anomalous foreign words), named entity recognition (NER) and subclassification. The proposed methodology starts with cleaning the data and then extracting structural and contextual features from th...

Learning Entity Representation for Named Entity Disambiguation

In this paper we present a novel disambiguation model, based on neural networks. Most existing studies focus on designing effective man-made features and complicated similarity measures to obtain better disambiguation performance. Instead, our method learns distributed representation of entity to measure similarity without man-made features. Entity representation consists of context document re...

Boosting for Named Entity Recognition