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An Enhanced Malay Named Entity Recognition using Combination Approach for Crime Textual Data Analysis

Siti Azirah Asmai, Muhammad Sharilazlan, Halizah Basiron, Sabrina Ahmad
2018 International Journal of Advanced Computer Science and Applications  
This paper presents an enhanced Malay Named Entity Recognition model using combination fuzzy c-means and K-Nearest Neighbours Algorithm method for crime analysis.  ...  Named Entity Recognition (NER) is one of the tasks in the information extraction.  ...  Unsupervised learning is also used in named entity recognition tasks. This learning-based is one of the approaches in solving the problems encountered in the task of named entity recognition. Li et al  ... 
doi:10.14569/ijacsa.2018.090960 fatcat:gv4hpq5gojgdposiu3onocpb3e

An Overview of Ontology Learning Process from Arabic Text

Mariam Muhammed, Nesrine Azim, Mervat Gheith
2020 The Egyptian Journal of Language Engineering  
There is a lot of research work interested in Ontology Learning for Arabic texts.  ...  Second, we make a research comparison based on the techniques used and their results. Third, we pointed out the limitations and comments of research works on Arabic Ontology Learning.  ...  (2012) presented an approach for integrating the rule-based approach with the Machine learning-based approach for Arabic named entity recognition [22] .  ... 
doi:10.21608/ejle.2020.19841.1000 fatcat:s3fe6obmnjdixlr4zqvhtcepsu

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

Gonçalo Carnaz, Mário Antunes, Vitor Beires Nogueira
2021 Data  
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.  ...  Table 5 . 5 Crime-related documents evaluation for named-entity recognition.  ... 
doi:10.3390/data6070071 fatcat:pu76uw2l7zd6vbpbnvxvcbbs2y

RuNNE-2022 Shared Task: Recognizing Nested Named Entities [article]

Ekaterina Artemova, Maxim Zmeev, Natalia Loukachevitch, Igor Rozhkov, Tatiana Batura, Vladimir Ivanov, Elena Tutubalina
2022 arXiv   pre-print
The RuNNE Shared Task approaches the problem of nested named entity recognition.  ...  The nestedness of named entities in NEREL reaches up to six levels. The RuNNE Shared Task explores two setups. (i) In the general setup all entities occur more or less with the same frequency.  ...  In both subtasks, the best model among machine learning approaches applied without additional manual data labeling was Machine Reading Comprehension model (Li et al., 2020) .  ... 
arXiv:2205.11159v1 fatcat:ymobblafinhwdhaulrekva4ccu

Economic Crime Detection Using Support Vector Machine Classification

Andriy Krysovatyy, Hrystyna Lipyanina-Goncharenko, Svitlana Sachenko, Oksana Desyatnyuk
2021 Modern Machine Learning Technologies  
machine learning, namely Support Vector Machine Classification, will develop a single software environment for rapid detection of economic crimes.  ...  To build the method, data from 1,100 companies operating in Ukraine were used. The data presented in in the set logical binary values are from 355 fictitious enterprises.  ...  Therefore, it is advisable to use machine learning algorithms that will more accurately determine economic crime.  ... 
dblp:conf/momlet/KrysovatyyLSD21 fatcat:nzkwv27q7bdtldfif5nhoa2bwu

A Probe on Crime Data in Various Domains

2020 International journal of recent technology and engineering  
The intention of this research article is to analyze the research articles that have been used in various criminal cases/activities that have taken place at different locations, and to identify the criteria  ...  Even as this article is being read, somewhere in a corner of the world, a man is currently suffering as a result of a crime. Crime has changed its shape over time according to the dimension of men.  ...  NER (Named Entity Recognition) techniques are used to extract the entity in text namely, person names, organizations, location, date and time.  ... 
doi:10.35940/ijrte.f8951.038620 fatcat:qqh3jx7uprey3bjmkk5aqs5tti

A Comparative Review of Machine Learning for Arabic Named Entity Recognition

Ramzi Esmail Salah, Lailatul Qadri binti Zakaria
2017 International Journal on Advanced Science, Engineering and Information Technology  
In general, ANER systems can be classified into three main approaches, namely, rule-based, machine-learning or hybrid systems.  ...  Arabic Named Entity Recognition (ANER) systems aim to identify and classify Arabic Named entities (NEs) within Arabic text.  ...  The most commonly published Machine Learning approaches for Named Entity Recognition are Supervised Learning (SL) techniques which represent the NER problem as a classification task and require the availability  ... 
doi:10.18517/ijaseit.7.2.1810 fatcat:ygldyzkm6rdpffsux5fqb7ywy4

Extracting and Visualizing Wildlife Trafficking Events from Wildlife Trafficking Reports [article]

Devin Coughlin, Maylee Gagnon, Victoria Grasso, Guanyi Mou, Kyumin Lee, Renata Konrad, Patricia Raxter, Meredith Gore
2022 arXiv   pre-print
We expanded Python spaCy's pre-trained pipeline and added a custom named entity ruler, which identified 15 fully correct and 36 partially correct events in 15 reports against an existing baseline, which  ...  These are accessible on our website, Wildlife Trafficking in Africa (  ...  Any opinions, findings and conclusions or recommendations expressed in this material are the author(s) and do not necessarily reflect those of the sponsors or the U.S. Government.  ... 
arXiv:2207.08217v1 fatcat:znugln4qr5bhrnb3pj3sme5ec4

An overview of information extraction techniques for legal document analysis and processing

Ashwini V. Zadgaonkar, Avinash J. Agrawal
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches.  ...  In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it.  ...  The crime base uses domain-specific manually crafted rule-based approach crime entities extraction by using techniques like Tokenization, POS tagging, NER, named entity disambiguation [36] contextual  ... 
doi:10.11591/ijece.v11i6.pp5450-5457 fatcat:cigtd4kh4vc4hhfnl32sss25ye

A Graph based Clustering Approach for Relation Extraction from Crime Data

P Das, A K Das, J Nayak, D Pelusi, W Ding.
2019 IEEE Access  
INDEX TERMS Crime analysis, named entity recognition, relation extraction, graph based clustering, cluster validation index.  ...  between the named entities present in the corpus by a hierarchical graph-based clustering technique.  ...  The objective of any named entity recognition (NER) system is to find out all the named entities present in a text document.  ... 
doi:10.1109/access.2019.2929597 fatcat:cehc4vldl5g57mof2imgsp3efu

Location Based Fake News Detection using Machine Learning

Prof. Rohit Nikam
2021 International Journal for Research in Applied Science and Engineering Technology  
Now a days lots of crime news incremented. In this system we can easy find which type of crime happened in particular city by using pin code.  ...  Machine learning plays important role to classify the information in different categories. This paper reviews finding different types of crime news in particular city and detected news fake or real.  ...  Name Entity Recognition in this step all type of classification will store i.e. location, name, map etc.  ... 
doi:10.22214/ijraset.2021.34939 fatcat:fim3fyrt3jc3jcfwrn3kcbqkva

Comparative study on corpus development for Malay investment fraud detection in website

M.M. Din, N.H.H. Hashim, M.M. Siraj
2018 Journal of Fundamental and Applied Sciences  
In this research, Part tagger (POS) and Named Entity Recognition (NER) tagger are selected.  ...  . tagger (POS) and Named Entity Recognition (NER) tagger are selected.  ...  Named Entity Recognition Generic NER systems tend to focus on finding the names, places and organization that are mentioned in text news.  ... 
doi:10.4314/jfas.v9i6s.62 fatcat:hu6kq5k7y5hkdldcuvyysexw44

Location reference recognition from texts: A survey and comparison [article]

Xuke Hu, Zhiyong Zhou, Hao Li, Yingjie Hu, Fuqiang Gu, Jens Kersten, Hongchao Fan, Friederike Klan
2022 arXiv   pre-print
, statistical learning-based, and hybrid approaches.  ...  Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts  ...  Statistical learning-based approaches can be further divided into two groups: learning-based named entity recognition (NER) and learning-based place name extraction (PNE).  ... 
arXiv:2207.01683v1 fatcat:xiy7az4veza6lm52c6yj7mnoe4

Challenges in Information Retrieval from Unstructured Arabic Data

Hussein Khalil, Taha Osman
2014 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation  
Regardless of geographic location and language used, much of this information is unstructured data.  ...  Moreover, to improve the intelligent exploration of unstructured documents in the Arabic domain.  ...  Two approaches were used in addressing the document; the first used a gazetteer to perform named entity recognition.  ... 
doi:10.1109/uksim.2014.115 dblp:conf/uksim/KhalilO14 fatcat:qkys7hsn6zcehj3ubxdrzhpwfm

A New Rule-Based Approach for Classical Arabic in Natural Language Processing

Ramzi Salah, Muaadh Mukred, Lailatul Qadri binti Zakaria, Rashad Ahmed, Hasan Sari, Ewa Rak
2022 Journal of Mathematics  
Named entity recognition (NER) is fundamental in several natural language processing applications.  ...  One of the most famous approaches to identify named entity is the rule-based approach. This paper introduces a rule-based NER method that can be used to examine Classical Arabic documents.  ...  [11] integrated machine learning with rule-based approaches for Arabic named entity recognition. e integration is done by using the output of the rule-based system as a feature of the machine learning  ... 
doi:10.1155/2022/7164254 fatcat:tppvgsmpgbdyhb5plxi7bqaz2i
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