Extraction of Semantic Relation Between Arabic Named Entities Using Different Kinds of Transducer Cascades

2018 ◽  
pp. 475-487
Author(s):  
Fatma Ben Mesmia ◽  
Kaouther Bouabidi ◽  
Kais Haddar ◽  
Nathalie Friburger ◽  
Denis Maurel
Keyword(s):  
Semantic Relation ◽  
Named Entities

Multi-Objective Optimization for the Joint Disambiguation of Nouns and Named Entities

2015 ◽  
Author(s):  
Dirk Weissenborn ◽  
Leonhard Hennig ◽  
Feiyu Xu ◽  
Hans Uszkoreit
Keyword(s):  
Multi Objective Optimization ◽  
Named Entities ◽  
Multi Objective

Contextual Recovery of Out-of-Lattice Named Entities in Automatic Speech Recognition

2019 ◽  
Author(s):  
Jack Serrino ◽  
Leonid Velikovich ◽  
Petar Aleksic ◽  
Cyril Allauzen
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Named Entities

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.

scholarly journals Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining

2012 ◽  
Vol 35 (1) ◽  
pp. 87-109 ◽  
Author(s):  
César de Pablo-Sánchez ◽  
Isabel Segura-Bedmar ◽  
Paloma Martínez ◽  
Ana Iglesias-Maqueda
Keyword(s):  
Text Mining ◽  
Named Entities ◽  
Linguistic Patterns ◽  
Multilingual Text
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