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

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.

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Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00008 ◽

2019 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Tomasz Oliwa ◽

Steven B. Maron ◽

Leah M. Chase ◽

Samantha Lomnicki ◽

Daniel V.T. Catenacci ◽

...

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Named Entity Recognition ◽

Entity Recognition ◽

Classification Model ◽

Supervised Machine Learning ◽

Named Entity ◽

Pathology Reports

PURPOSE Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions. PATIENTS AND METHODS Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step. RESULTS We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance. CONCLUSION Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

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A Survey of Arabic Named Entity Recognition and Classification

Computational Linguistics ◽

10.1162/coli_a_00178 ◽

2014 ◽

Vol 40 (2) ◽

pp. 469-510 ◽

Cited By ~ 62

Author(s):

Khaled Shaalan

Keyword(s):

Language Processing ◽

Named Entity Recognition ◽

Relevant Information ◽

Arabic Language ◽

Entity Recognition ◽

Named Entities ◽

Linguistic Resources ◽

Named Entity ◽

To Receive ◽

Made In

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.

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SCIENTIFIC NAMED ENTITY RECOGNITION WITH THE HELP OF MODERN METHODS

Bulletin Series of Physics & Mathematical Sciences ◽

10.51889/2021-3.1728-7901.11 ◽

2021 ◽

Vol 75 (3) ◽

pp. 94-99

Author(s):

A.M. Yelenov ◽

◽

A.B. Jaxylykova ◽

Keyword(s):

Machine Learning ◽

Language Processing ◽

Named Entity Recognition ◽

Recognition Task ◽

Entity Recognition ◽

Support Vector ◽

Scientific Article ◽

Natural Languages ◽

Named Entity ◽

Learning Area

This research focuses on a comparative study of the Named Entity Recognition task for scientific article texts. Natural language processing could be considered as one of the cornerstones in the machine learning area which devotes its attention to the problems connected with the understanding of different natural languages and linguistic analysis. It was already shown that current deep learning techniques have a good performance and accuracy in such areas as image recognition, pattern recognition, computer vision, that could mean that such technology probably would be successful in the neuro-linguistic programming area too and lead to a dramatic increase on the research interest on this topic. For a very long time, quite trivial algorithms have been used in this area, such as support vector machines or various types of regression, basic encoding on text data was also used, which did not provide high results. The following dataset was used to process the experiment models: Dataset Scientific Entity Relation Core. The algorithms used were Long short-term memory, Random Forest Classifier with Conditional Random Fields, and Named-entity recognition with Bidirectional Encoder Representations from Transformers. In the findings, the metrics scores of all models were compared to each other to make a comparison. This research is devoted to the processing of scientific articles, concerning the machine learning area, because the subject is not investigated on enough properly level.The consideration of this task can help machines to understand natural languages better, so that they can solve other neuro-linguistic programming tasks better, enhancing scores in common sense.

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Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks

BioMed Research International ◽

10.1155/2014/240403 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 49

Author(s):

Buzhou Tang ◽

Hongxin Cao ◽

Xiaolong Wang ◽

Qingcai Chen ◽

Hua Xu

Keyword(s):

Machine Learning ◽

Language Processing ◽

Named Entity Recognition ◽

Entity Recognition ◽

Biomedical Domain ◽

Crucial Step ◽

Named Entity ◽

Different Types ◽

Word Representation ◽

Biomedical Named Entity Recognition

Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements inF-measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.

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Evaluating named entity recognition tools for extracting social networks from novels

PeerJ Computer Science ◽

10.7717/peerj-cs.189 ◽

2019 ◽

Vol 5 ◽

pp. e189 ◽

Cited By ~ 2

Author(s):

Niels Dekker ◽

Tobias Kuhn ◽

Marieke van Erp

Keyword(s):

Social Networks ◽

Social Interactions ◽

Language Processing ◽

Named Entity Recognition ◽

Entity Recognition ◽

Computer Assisted ◽

Early 20Th Century ◽

Automatic Extraction ◽

Named Entities ◽

Named Entity

The analysis of literary works has experienced a surge in computer-assisted processing. To obtain insights into the community structures and social interactions portrayed in novels, the creation of social networks from novels has gained popularity. Many methods rely on identifying named entities and relations for the construction of these networks, but many of these tools are not specifically created for the literary domain. Furthermore, many of the studies on information extraction from literature typically focus on 19th and early 20th century source material. Because of this, it is unclear if these techniques are as suitable to modern-day literature as they are to those older novels. We present a study in which we evaluate natural language processing tools for the automatic extraction of social networks from novels as well as their network structure. We find that there are no significant differences between old and modern novels but that both are subject to a large amount of variance. Furthermore, we identify several issues that complicate named entity recognition in our set of novels and we present methods to remedy these. We see this work as a step in creating more culturally-aware AI systems.

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Statistical Method for Named Entity Recognition in Telugu, an Indian Language

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3500.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 4211-4216

Keyword(s):

Language Processing ◽

Conditional Random Fields ◽

Named Entity Recognition ◽

Entity Recognition ◽

Semantic Features ◽

Indian Language ◽

Named Entities ◽

Maximum Entropy Models ◽

Named Entity ◽

Proper Nouns

One of the important tasks of Natural Language Processing (NLP) is Named Entity Recognition (NER). The primary operation of NER is to identify proper nouns i.e. to locate all the named entities in the text and tag them as certain named entity categories such as Entity, Time expression and Numeric expression. In the previous works, NER for Telugu language is addressed with Conditional Random Fields (CRF) and Maximum Entropy models however they failed to handle ambiguous named entity tags for the same named entity. This paper presents a hybrid statistical system for Named Entity Recognition in Telugu language in which named entities are identified by both dictionary-based approach and statistical Hidden Markov Model (HMM). The proposed method uses Lexicon-lookup dictionary and contexts based on semantic features for predicting named entity tags. Further HMM is used to resolve the named entity ambiguities in predicted named entity tags. The present work reports an average accuracy of 86.3% for finding the named entities

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Evaluating social network extraction for classic and modern fiction literature

10.7287/peerj.preprints.27263 ◽

2018 ◽

Author(s):

Niels Dekker ◽

Tobias Kuhn ◽

Marieke van Erp

Keyword(s):

Social Networks ◽

Science Fiction ◽

19Th Century ◽

Language Processing ◽

Named Entity Recognition ◽

Entity Recognition ◽

Computer Assisted ◽

Named Entities ◽

Named Entity ◽

Modern Fiction

The analysis of literary works has experienced a surge in computer-assisted processing. To obtain insights into the community structures and social interactions portrayed in novels the creation of social networks from novels has gained popularity. Many methods rely on identifying named entities and relations for the construction of these networks, but many of these tools are not specifically created for the literary domain. Furthermore, many of the studies on information extraction from literature typically focus on 19th century source material. Because of this, it is unclear if these techniques are as suitable to modern-day science fiction and fantasy literature as they are to those 19th century classics. We present a study to compare classic literature to modern literature in terms of performance of natural language processing tools for the automatic extraction of social networks as well as their network structure. We find that there are no significant differences between the two sets of novels but that both are subject to a high amount of variance. Furthermore, we identify several issues that complicate named entity recognition in modern novels and we present methods to remedy these.

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Techniques for Named Entity Recognition on Arabic-English Code-Mixed Data

International Journal of Robotic Computing ◽

10.35708/tai1868-126245 ◽

2019 ◽

pp. 44-63

Author(s):

Caroline Sabty ◽

Ahmed Sherif ◽

Mohamed Elmahdy ◽

Slim Abdennadher

Keyword(s):

Language Processing ◽

Named Entity Recognition ◽

Arab Countries ◽

Entity Recognition ◽

Mixed Data ◽

Word Embeddings ◽

Named Entities ◽

Named Entity ◽

Code Mixing ◽

Social Media Platforms

As a result of globalization and better quality of education, a signifcant percentage of the population in Arab countries have become bilingual/multilingual. This has raised the frequency of code-switching and code-mixing among Arabs in daily communication. Consequently, huge amount of Code-Mixed (CM) content can be found on different social media platforms. Such data could be analyzed and used in different Natural Language Processing (NLP) tasks to tackle the challenges emerging due to this multilingual phenomenon. Named-Entity Recognition (NER) is one of the major tasks for several NLP systems. It is the process of identifying named entities in text. However, there is a lack of annotated CM data and resources for such task. This work aims at collecting and building the first annotated CM Arabic-English corpus for NER. Furthermore, we constructed a baseline NER system using deep neural networks and word embeddings for Arabic-English CM text. Moreover, we investigated the usage of different types of classical and contextual pre-trained word embeddings on our system. The highest NER system achieved an F1-score of 77.69% by combining classical and contextual word embeddings.

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Domain-Transferable Method for Named Entity Recognition Task

10.5121/csit.2020.101407 ◽

2020 ◽

Author(s):

Vladislav Mikhailov ◽

Tatiana Shavrina

Keyword(s):

Language Processing ◽

Question Answering ◽

Named Entity Recognition ◽

Recognition Task ◽

Entity Recognition ◽

Neural Models ◽

Named Entities ◽

Named Entity ◽

Domain Specific ◽

Human Effort

Named Entity Recognition (NER) is a fundamental task in the fields of natural language processing and information extraction. NER has been widely used as a standalone tool or an essential component in a variety of applications such as question answering, dialogue assistants and knowledge graphs development. However, training reliable NER models requires a large amount of labelled data which is expensive to obtain, particularly in specialized domains. This paper describes a method to learn a domain-specific NER model for an arbitrary set of named entities when domain-specific supervision is not available. We assume that the supervision can be obtained with no human effort, and neural models can learn from each other. The code, data and models are publicly available.

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Using Named Entities for Recognizing Family Relationships

10.5753/kdmile.2021.17457 ◽

2021 ◽

Author(s):

E. Oliveira ◽

G. Dias ◽

J. Lima ◽

J. P. C. Pirovani

Keyword(s):

Language Processing ◽

Family Relationships ◽

Named Entity Recognition ◽

Entity Recognition ◽

Hard Problem ◽

Named Entities ◽

Named Entity ◽

Hybrid Approaches ◽

Local Grammar ◽

F Measure

Named Entity Recognition problem’s objective is to automatically identify and classify entities like persons, places,organizations, and so forth. That is an area in Natural Language Processing and Information Extraction. NamedEntity Recognition is important because it is a fundamental step of preprocessing for several applications like relationextraction. However, it is a hard problem to solve as several categories of named entities are written similarly andthey appear in similar contexts. To accomplish it, we can use some hybrid approaches. Nevertheless, in this presentstudy, we use linguistic flavor by applying Local Grammar and Cascade of Transducers. Local Grammars are used torepresent the rules of a particular linguistic structure. They are often built manually to describe the entities we aimto recognize. In our study, we adapted a Local Grammar to improve the Recognition of Named Entities. The resultsshow an improvement of up to 7% on the F-measure metric in relation to the previous Local Grammar. Also, we builtanother Local Grammar to recognize family relationships from the improved Local Grammar. We present a practicalapplication for the extracted relationships using Prolog.

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