Named Entity Recognition for the Indonesian Language: Combining Contextual, Morphological and Part-of-Speech Features into a Knowledge Engineering Approach

2005 ◽  
pp. 57-69 ◽  
Author(s):  
Indra Budi ◽  
Stéphane Bressan ◽  
Gatot Wahyudi ◽  
Zainal A. Hasibuan ◽  
Bobby A. A. Nazief
Keyword(s):  
Knowledge Engineering ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Engineering Approach ◽  
Part Of Speech ◽  
Speech Features

Research on Chinese Named Entity Recognition Based on Ontology

2012 ◽  
Vol 195-196 ◽  
pp. 1180-1185
Author(s):  
Wei Li Chang ◽  
Fang Luo ◽  
Ji Lai Qian
Keyword(s):  
Language Processing ◽  
Conditional Random Fields ◽  
Critical Role ◽  
Named Entity Recognition ◽  
Recall Rate ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech ◽  
Speech Features ◽  
Precision Rate

As a critical role in many Natural Language Processing (NLP) applications, such as Information Extraction, Machine Translation etc, Chinese Named Entity Recognition (NER) remains a challenging task because of its characteristics. This paper proposes a method of Chinese NER, which combining Conditional Random Fields (CRFs) model with domain ontology as a semantic feature besides word and part of speech features. Experiments were made to compare the two kinds of feature templates, and the precision rate and recall rate of Chinese NER rose to 90.86% and 88.23%, which showed remarkable performance of the proposed approach. Combination of ontology and CRFs method increased effectively the precision and recall of Chinese NER.

scholarly journals Learning Task-Specific Representation for Novel Words in Sequence Labeling

2019 ◽  
Author(s):  
Minlong Peng ◽  
Qi Zhang ◽  
Xiaoyu Xing ◽  
Tao Gui ◽  
Jinlan Fu ◽  
...  
Keyword(s):  
Empirical Studies ◽  
Named Entity Recognition ◽  
Learning Task ◽  
Training Data ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Sequence Labeling ◽  
Part Of Speech ◽  
Word Representation

Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor for appreciable performance. This is commonly referred to as the out-of-vocabulary (OOV) problem. In this work, we address the OOV problem in sequence labeling using only training data of the task. To this end, we propose a novel method to predict representations for OOV words from their surface-forms (e.g., character sequence) and contexts. The method is specifically designed to avoid the error propagation problem suffered by existing approaches in the same paradigm. To evaluate its effectiveness, we performed extensive empirical studies on four part-of-speech tagging (POS) tasks and four named entity recognition (NER) tasks. Experimental results show that the proposed method can achieve better or competitive performance on the OOV problem compared with existing state-of-the-art methods.

scholarly journals A Probability based Classification of Named Entities for Malayalam Language combining Word, Part of Speech and Lexicalized features

2019 ◽  
Vol 8 (2) ◽  
pp. 839-842
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Supervised Machine Learning ◽  
Named Entities ◽  
Named Entity ◽  
Domain Specific ◽  
Part Of Speech ◽  
Classification Probability ◽  
Malayalam Language

Named Entity Recognition is the process wherein named entities which are designators of a sentence are identified. Designators of a sentence are domain specific. The proposed system identifies named entities in Malayalam language belonging to tourism domain which generally includes names of persons, places, organizations, dates etc. The system uses word, part of speech and lexicalized features to find the probability of a word belonging to a named entity category and to do the appropriate classification. Probability is calculated based on supervised machine learning using word and part of speech features present in a tagged training corpus and using certain rules applied based on lexicalized features.

Advances in Computational Linguistics and Text Processing Frameworks

2020 ◽  
pp. 217-244
Author(s):  
Ayush Srivastav ◽  
Hera Khan ◽  
Amit Kumar Mishra
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

scholarly journals An Arabic Dataset for Disease Named Entity Recognition with Multi-Annotation Schemes

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 60
Author(s):  
Nasser Alshammari ◽  
Saad Alanazi
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Definite Article ◽  
Linguistic Features ◽  
Annotation Scheme ◽  
Named Entity ◽  
Part Of Speech ◽  
Annotation Process ◽  
Arabic Natural Language Processing

This article outlines a novel data descriptor that provides the Arabic natural language processing community with a dataset dedicated to named entity recognition tasks for diseases. The dataset comprises more than 60 thousand words, which were annotated manually by two independent annotators using the inside–outside (IO) annotation scheme. To ensure the reliability of the annotation process, the inter-annotator agreements rate was calculated, and it scored 95.14%. Due to the lack of research efforts in the literature dedicated to studying Arabic multi-annotation schemes, a distinguishing and a novel aspect of this dataset is the inclusion of six more annotation schemes that will bridge the gap by allowing researchers to explore and compare the effects of these schemes on the performance of the Arabic named entity recognizers. These annotation schemes are IOE, IOB, BIES, IOBES, IE, and BI. Additionally, five linguistic features, including part-of-speech tags, stopwords, gazetteers, lexical markers, and the presence of the definite article, are provided for each record in the dataset.

Named entity recognition in Bengali using system combination

2014 ◽  
Vol 37 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Asif Ekbal ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Machine Learning Algorithms ◽  
Unlabeled Data ◽  
Entity Recognition ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
System Combination ◽  
Named Entity ◽  
Part Of Speech

This paper reports a voted Named Entity Recognition (NER) system that exploits appropriate unlabeled data. Initially, we develop NER systems using the supervised machine learning algorithms such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM). Each of these models makes use of the language independent features in the form of different contextual and orthographic word-level features along with the language dependent features extracted from the Part-of-Speech (POS) tagger and gazetteers. Context patterns generated from the unlabeled data using an active learning method are also used as the features in each of the classifiers. A semi-supervised method is proposed to describe the measures to automatically select effective unlabeled documents as well as sentences from the unlabeled data. Finally, the supervised models are combined together into a final system by defining appropriate weighted voting technique. Experimental results for a resource-poor language like Bengali show the effectiveness of the proposed approach with the overall recall, precision and F-measure values of 93.81%, 92.18% and 92.98%, respectively.

scholarly journals Named entity recognition in texts with the help of part of speech tagging

2018 ◽  
pp. 74-83
Author(s):  
M. Bevza
Keyword(s):  
State Of The Art ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Recent Developments ◽  
Future Work

We analyze neural network architectures that yield state of the art results on named entity recognition task and propose a number of new architectures for improving results even further. We have analyzed a number of ideas and approaches that researchers have used to achieve state of the art results in a variety of NLP tasks. In this work, we present a few architectures which we consider to be most likely to improve the existing state of the art solutions for named entity recognition task and part of speech tasks. The architectures are inspired by recent developments in multi-task learning. This work tests the hypothesis that NER and POS are related tasks and adding information about POS tags as input to the network can help achieve better NER results. And vice versa, information about NER tags can help solve the task of POS tagging. This work also contains the implementation of the network and results of the experiments together with the conclusions and future work.

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