Machine-learning methods for text named entity recognition

2016 ◽  
pp. 150-157
Author(s):  
O.O. Marchenko ◽  
Keyword(s):  
Machine Learning ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Methods ◽  
Named Entities ◽  
Named Entity ◽  
Machine Learning Methods ◽  
Multi Classification

The article describes machine learning methods for the named entity recognition. To build named entity classifiers two basic models of machine learning, The Naїve Bayes and Conditional Random Fields, were used. A model for multi-classification of named entities using Error Correcting Output Codes was also researched. The paper describes a method for classifiers' training and the results of test experiments. Conditional Random Fields overcome other models in precision and recall evaluations.

scholarly journals Conditional Random Fields for Biomedical Named Entity Recognition Revisited

2020 ◽  
Author(s):  
Xie-Yuan Xie
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biomedical Domain ◽  
Minimal Set ◽  
Named Entities ◽  
Named Entity ◽  
Biomedical Texts ◽  
Biomedical Named Entity Recognition

Abstract Named Entity Recognition (NER) is a key task which automatically extracts Named Entities (NE) from the text. Names of persons, places, date and time are examples of NEs. We are applying Conditional Random Fields (CRFs) for NER in biomedical domain. Examples of NEs in biomedical texts are gene, proteins. We used a minimal set of features to train CRF algorithm and obtained a good results for biomedical texts.

scholarly journals An Annotated Corpus of Crime-Related Portuguese Documents for NLP and Machine Learning Processing

Data ◽  
2021 ◽  
Vol 6 (7) ◽  
pp. 71
Author(s):  
Gonçalo Carnaz ◽  
Mário Antunes ◽  
Vitor Beires Nogueira
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Automatic Identification ◽  
Named Entities ◽  
Related Data ◽  
Named Entity ◽  
Chain Of Custody ◽  
Evidence Collection

Criminal investigations collect and analyze the facts related to a crime, from which the investigators can deduce evidence to be used in court. It is a multidisciplinary and applied science, which includes interviews, interrogations, evidence collection, preservation of the chain of custody, and other methods and techniques of investigation. These techniques produce both digital and paper documents that have to be carefully analyzed to identify correlations and interactions among suspects, places, license plates, and other entities that are mentioned in the investigation. The computerized processing of these documents is a helping hand to the criminal investigation, as it allows the automatic identification of entities and their relations, being some of which difficult to identify manually. There exists a wide set of dedicated tools, but they have a major limitation: they are unable to process criminal reports in the Portuguese language, as an annotated corpus for that purpose does not exist. This paper presents an annotated corpus, composed of a collection of anonymized crime-related documents, which were extracted from official and open sources. The dataset was produced as the result of an exploratory initiative to collect crime-related data from websites and conditioned-access police reports. The dataset was evaluated and a mean precision of 0.808, recall of 0.722, and F1-score of 0.733 were obtained with the classification of the annotated named-entities present in the crime-related documents. This corpus can be employed to benchmark Machine Learning (ML) and Natural Language Processing (NLP) methods and tools to detect and correlate entities in the documents. Some examples are sentence detection, named-entity recognition, and identification of terms related to the criminal domain.

Named entity recognition based on conditional random fields

2017 ◽  
Vol 22 (S3) ◽  
pp. 5195-5206 ◽  
Author(s):  
Shengli Song ◽  
Nan Zhang ◽  
Haitao Huang
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity
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