scholarly journals Arabic Location Name Annotations and Applications

2020 ◽  
Author(s):  
Omar ASBAYOU
Keyword(s):  
Classification System ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Syntactic Analysis ◽  
Word Sense ◽  
Semantic Classification ◽  
Named Entity ◽  
Semantic Data ◽  
Linguistic Approach

This paper show how location named entity (LNE) extraction and annotation, which makes part of our named entity recognition (NER) systems, is an important task in managing the great amount of data. In this paper, we try to explain our linguistic approach in our rule-based LNE recognition and classification system based on syntactico-semantic patterns. To reach good results, we have taken into account morpho-syntactic information provided by morpho-syntactic analysis based on DIINAR database, and syntactico-semantic classification of both location name trigger words (TW) and extensions. Formally, different trigger word sense implies different syntactic entity structures. We also show the semantic data that our LNE recognition and classification system can provide to both information extraction (IE) and information retrieval(IR).The XML database output of the LNE system constituted an important resource for IE and IR. Future project will improve this processing output in order to exploit it in computerassisted Translation (CAT).

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.

scholarly journals Improving classification of low-resource COVID-19 literature by using Named Entity Recognition

2021 ◽  
Vol 19 (3) ◽  
pp. e22
Author(s):  
Oscar Lithgow-Serrano ◽  
Joseph Cornelius ◽  
Vani Kanjirangat ◽  
Carlos-Francisco Méndez-Cruz ◽  
Fabio Rinaldi
Keyword(s):  
Classification Scheme ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biological Databases ◽  
Proof Of Concept ◽  
Baseline Model ◽  
Named Entities ◽  
Named Entity ◽  
Automatic Document Classification

Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) Clinical repository—a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice—where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene’s Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE’s origin was useful to classify document types and NE’s type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed.

scholarly journals Automatic Arabic Named Entity Extraction and Classification for Information Retrieval

2020 ◽  
Vol 9 (6) ◽  
pp. 1-22
Author(s):  
Omar ASBAYOU
Keyword(s):  
Information Retrieval ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Rule Based ◽  
Named Entity ◽  
Named Entity Extraction ◽  
Name Entity ◽  
System Output

This article tries to explain our rule-based Arabic Named Entity recognition (NER) and classification system. It is based on lists of classified proper names (PN) and particularly on syntactico-semantic patterns resulting in fine classification of Arabic NE. These patterns use syntactico-semantic combination of morpho-syntactic and syntactic entities. It also uses lexical classification of trigger words and NE extensions. These linguistic data are essential not only to name entity extraction but also to the taxonomic classification and to determining the NE frontiers. Our method is also based on the contextualisation and on the notion of NE class attributes and values. Inspired from X-bar theory and immediate constituents, we built a rule-based NER system composed of five levels of syntactico-semantic combination. We also show how the fine NE annotations in our system output (XML database) is exploited in information retrieval and information extraction.

Effective and Efficient Classification of Topically-Enriched Domain-Specific Text Snippets

2015 ◽  
Vol 6 (3) ◽  
pp. 1-17 ◽  
Author(s):  
Marco Spruit ◽  
Bas Vlug
Keyword(s):  
Production Systems ◽  
Named Entity Recognition ◽  
Classification Problem ◽  
Entity Recognition ◽  
Classification Error ◽  
Named Entity ◽  
Domain Specific ◽  
Stop Word ◽  
Different Types

Due to the explosive growth in the amount of text snippets over the past few years and their sparsity of text, organizations are unable to effectively and efficiently classify them, missing out on business opportunities. This paper presents TETSC: the Topically-Enriched Text Snippet Classification method. TETSC aims to solve the classification problem for text snippets in any domain. TETSC recognizes that there are different types of text snippets and, therefore, allows for stop word removal, named-entity recognition, and topical enrichment for the different types of text snippets. TETSC has been implemented in the production systems of a personal finance organization, which resulted in a classification error reduction of over 21%. Highlights: The authors create the TETSC method for classifying topically-enriched text snippets; the authors differentiate between different types of text snippets; the authors show a successful application of Named-Entity Recognition to text snippets; using multiple enrichment strategies appears to reduce effectivity.

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