Named entity recognition

Meselhi, M., H. Abo Bakr, I. Ziedan, and K. Shaalan, "Hybrid Named Entity Recognition - Application to Arabic Language", The International Conference on Computer Engineering & Systems (ICCES), Egypt, 23 December, 2015. Abstractarabic_hybrid_ner.pdf

Most Named Entity Recognition (NER) systems follow either a rule-based approach or machine learning approach. In this paper, we introduce out attempt at developing a hybrid NER system, which combines the rule-based approach with a machine learning approach in order to obtain the advantages of both approaches and
overcomes their problems. The system is able to recognize eight types of named entities including Location,
Person, Organization, Date, Time, Price, Measurement and Percent. Experimental results on ANERcorp dataset indicated that our hybrid approach outperforms the rule-based approach and the machine learning approach when
they are processed separately. Moreover, our hybrid approach outperforms the state-of-the-art of Arabic NER.

Meselhi, M., H. Abo Bakr, I. Ziedan, and K. Shaalan, "A Novel Hybrid Approach to Arabic Named Entity Recognition", Machine Translation: Communications in Computer and Information Science: Springer, 2014. Abstracthybrid_arabic_ner_2014.pdf

Named Entity Recognition (NER) task is an essential preprocessing task for many Natural Language Processing (NLP) applications such as text summarization, document categorization, Information Retrieval, among others. NER systems follow either rule-based approach or machine learning approach. In this paper, we introduce a novel NER system for Arabic using a hybrid approach, which combines a rule-based approach and a machine learning approach in order to improve the performance of Arabic NER. The system is able to recognize three types of named entities, including Person, Location and Organization. Experimental results on ANERcorp dataset showed that our hybrid approach has achieved better performance than using the rule-based approach and the machine learning approach when they are processed separately. It also outperforms the state-of-the-art hybrid Arabic NER systems.

Shaalan, K., "A Survey of Arabic Named Entity Recognition and Classification", Computational LinguisticsComputational Linguistics, vol. 40, issue 2: MIT Press, pp. 469 - 510, 2013, 2014. Abstractcoli_a_00178.pdfWebsite

As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.

Shaalan, K., and M. Oudah, "A hybrid approach to Arabic named entity recognition", Journal of Information Science, vol. 40, no. 1, pp. 67-87, 2014. Abstractjis_arabicner.pdfWebsite

In this paper, we propose a hybrid named entity recognition (NER) approach that takes the advantages of rule-based and machine learning-based approaches in order to improve the overall system performance and overcome the knowledge elicitation bottleneck and the lack of resources for underdeveloped languages that require deep language processing, such as Arabic. The complexity of Arabic poses special challenges to researchers of Arabic NER, which is essential for both monolingual and multilingual applications. We used the hybrid approach to develop an Arabic NER system that is capable of recognizing 11 types of Arabic named entities: Person, Location, Organization, Date, Time, Price, Measurement, Percent, Phone Number, ISBN and File Name. Extensive experiments were conducted using decision trees, Support Vector Machines and logistic regression classifiers to evaluate the system performance. The empirical results indicate that the hybrid approach outperforms both the rule-based and the ML-based approaches when they are processed independently. More importantly, our system outperforms the state-of-the-art of Arabic NER in terms of accuracy when applied to ANERcorp standard dataset, with F-measures 0.94 for Person, 0.90 for Location and 0.88 for Organization.

Oudah, M., and K. Shaalan, "Person Name Recognition Using the Hybrid Approach", Natural Language Processing and Information Systems, vol. 7934, Berlin Heidelberg, Springer , pp. 237-248, 2013. Abstractperson_ner_using_hyprid_approach.pdf

Arabic Person Name Recognition has been tackled mostly using either of two approaches: a rule-based or Machine Learning (ML) based approach, with their strengths and weaknesses. In this paper, the problem of Arabic Person Name Recognition is tackled through integrating the two approaches together in a pipelined process to create a hybrid system with the aim of enhancing the overall performance of Person Name Recognition tasks. Extensive experiments are conducted using three different ML classifiers to evaluate the overall performance of the hybrid system. The empirical results indicate that the hybrid approach outperforms both the rule-based and the ML-based approaches. Moreover, our system outperforms the state-of-the-art of Arabic Person Name Recognition in terms of accuracy when applied to ANERcorp dataset, with precision 0.949, recall 0.942 and f-measure 0.945.

Abdallah, S., K. Shaalan, and M. Shoaib, "Integrating Rule-Based System with Classification for Arabic Named Entity Recognition", Computational Linguistics and Intelligent Text Processing, vol. 7181, Berlin, Heidelberg, Springer , pp. 311-322, 2012. Abstracthybrid_nera_2012.pdf

Named Entity Recognition (NER) is a subtask of information extraction that seeks to recognize and classify named entities in unstructured text into predefined categories such as the names of persons, organizations, locations, etc. The majority of researchers used machine learning, while few researchers used handcrafted rules to solve the NER problem. We focus here on NER for the Arabic language (NERA), an important language with its own distinct challenges. This paper proposes a simple method for integrating machine learning with rule-based systems and implement this proposal using the state-of-the-art rule-based system for NERA. Experimental evaluation shows that our integrated approach increases the F-measure by 8 to 14% when compared to the original (pure) rule based system and the (pure) machine learning approach, and the improvement is statistically significant for different datasets. More importantly, our system outperforms the state-of-the-art machine-learning system in NERA over a benchmark dataset.

Oudah, M., and K. Shaalan, "A Pipeline Arabic Named Entity Recognition Using a Hybrid Approach", The International Conference on Computational Linguistics (COLING), Mumbai, India, 14 December, 2012. Abstractpipeline_ner.pdf

Most Arabic Named Entity Recognition (NER) systems have been developed using either of two approaches: a rule-based or Machine Learning (ML) based approach, with their strengths and weaknesses. In this paper, the problem of Arabic NER is tackled through integrating the two approaches together in a pipelined process to create a hybrid system with the aim of enhancing the overall performance of NER tasks. The proposed system is capable of recognizing 11 different types of named entities (NEs): Person, Location, Organization, Date, Time, Price, Measurement, Percent, Phone Number, ISBN and File Name. Extensive experiments are conducted using three different ML classifiers to evaluate the overall performance of the hybrid system. The empirical results indicate that the hybrid approach outperforms both the rule-based and the ML-based approaches. Moreover, our system outperforms the state-of-the-art of Arabic NER in terms of accuracy when applied to ANERcorp dataset, with f-measures 94.4% for Person, 90.1% for Location, and 88.2% for Organization.

Shaalan, K., and H. Raza, "Arabic Named Entity Recognition from Diverse Text Types", Advances in Natural Language Processing, vol. 5221: Springer Berlin Heidelberg, pp. 440-451, 2008. Abstractgotal_nera_.pdf

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 Arabic natural language processing in general. In this paper, we present the results of our attempt at the recognition and extraction of 10 most important named entities in Arabic script; the person name, location, company, date, time, price, measurement, phone number, ISBN and file name. We developed the system, Name Entity Recognition for Arabic (NERA), using a rule-based approach. The system consists of a whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. NERA is evaluated using our own corpora that are tagged in a semi-automated way, and the performance results achieved were satisfactory in terms of precision, recall, and f-measure.

Shaalan, K., and H. Raza, "Person name entity recognition for Arabic", ACL 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources, Prague, Czech Republic, Association for Computational Linguistics, pp. 17–24, 28 June, 2007. Abstractpera_cameraready.pdf

Named entity recognition (NER) is nowadays an important task, which is responsible for the identification of proper names in text and their classification as different types of named entity such as people, locations, and organizations. In this paper, we present our attempt at the recognition and extraction of the most important proper name entity, that is, the person name, for the Arabic language. We developed the system, Person Name Entity Recognition for Arabic (PERA), using a rule-based approach. The system consists of a lexicon, in the form of gazetteer name lists, and a grammar, in the form of regular expressions, which are responsible for recognizing person name entities. The PERA system is evaluated using a corpus that is tagged in a semi-automated way. The system performance results achieved were satisfactory and confirm to the targets set forth for the precision, recall, and f-measure.

Shaalan, K., and H. Raza, "NERA: Named Entity Recognition for Arabic", J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 8, New York, NY, USA, John Wiley & Sons, Inc., pp. 1652–1663, 2009. Abstractnera_paper.pdfWebsite

Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products revolving around natural language processing tasks. Many researchers have attacked the name identification problem in a variety of languages, but only a few limited research efforts have focused on named entity recognition for Arabic script. This is due to the lack of resources for Arabic named entities and the limited amount of progress made in Arabic natural language processing in general. In this article, we present the results of our attempt at the recognition and extraction of the 10 most important categories of named entities in Arabic script: the person name, location, company, date, time, price, measurement, phone number, ISBN, and file name. We developed the system Named Entity Recognition for Arabic (NERA) using a rule-based approach. The resources created are: a Whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. A filtration mechanism is used that serves two different purposes: (a) revision of the results from a named entity extractor by using metadata, in terms of a Blacklist or rejecter, about ill-formed named entities and (b) disambiguation of identical or overlapping textual matches returned by different name entity extractors to get the correct choice. In NERA, we addressed major challenges posed by NER in the Arabic language arising due to the complexity of the language, peculiarities in the Arabic orthographic system, non-standardization of the written text, ambiguity, and lack of resources. NERA has been effectively evaluated using our own tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.}

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