scholarly journals An Arabic Dataset for Disease Named Entity Recognition with Multi-Annotation Schemes

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 60
Author(s):  
Nasser Alshammari ◽  
Saad Alanazi
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Definite Article ◽  
Linguistic Features ◽  
Annotation Scheme ◽  
Named Entity ◽  
Part Of Speech ◽  
Annotation Process ◽  
Arabic Natural Language Processing

This article outlines a novel data descriptor that provides the Arabic natural language processing community with a dataset dedicated to named entity recognition tasks for diseases. The dataset comprises more than 60 thousand words, which were annotated manually by two independent annotators using the inside–outside (IO) annotation scheme. To ensure the reliability of the annotation process, the inter-annotator agreements rate was calculated, and it scored 95.14%. Due to the lack of research efforts in the literature dedicated to studying Arabic multi-annotation schemes, a distinguishing and a novel aspect of this dataset is the inclusion of six more annotation schemes that will bridge the gap by allowing researchers to explore and compare the effects of these schemes on the performance of the Arabic named entity recognizers. These annotation schemes are IOE, IOB, BIES, IOBES, IE, and BI. Additionally, five linguistic features, including part-of-speech tags, stopwords, gazetteers, lexical markers, and the presence of the definite article, are provided for each record in the dataset.

Advances in Computational Linguistics and Text Processing Frameworks

2020 ◽  
pp. 217-244
Author(s):  
Ayush Srivastav ◽  
Hera Khan ◽  
Amit Kumar Mishra
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

Research on Chinese Named Entity Recognition Based on Ontology

2012 ◽  
Vol 195-196 ◽  
pp. 1180-1185
Author(s):  
Wei Li Chang ◽  
Fang Luo ◽  
Ji Lai Qian
Keyword(s):  
Language Processing ◽  
Conditional Random Fields ◽  
Critical Role ◽  
Named Entity Recognition ◽  
Recall Rate ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech ◽  
Speech Features ◽  
Precision Rate

As a critical role in many Natural Language Processing (NLP) applications, such as Information Extraction, Machine Translation etc, Chinese Named Entity Recognition (NER) remains a challenging task because of its characteristics. This paper proposes a method of Chinese NER, which combining Conditional Random Fields (CRFs) model with domain ontology as a semantic feature besides word and part of speech features. Experiments were made to compare the two kinds of feature templates, and the precision rate and recall rate of Chinese NER rose to 90.86% and 88.23%, which showed remarkable performance of the proposed approach. Combination of ontology and CRFs method increased effectively the precision and recall of Chinese NER.

scholarly journals Indonesian Sentence Boundary Detection using Deep Learning Approaches

2021 ◽  
Vol 4 (1) ◽  
pp. 38
Author(s):  
Joan Santoso ◽  
Esther Irawati Setiawan ◽  
Christian Nathaniel Purwanto ◽  
Fachrul Kurniawan
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Approaches ◽  
Named Entity ◽  
Sequence Labeling ◽  
Part Of Speech ◽  
Sentence Patterns ◽  
Sentence Boundary

Detecting the sentence boundary is one of the crucial pre-processing steps in natural language processing. It can define the boundary of a sentence since the border between a sentence, and another sentence might be ambiguous. Because there are multiple separators and dynamic sentence patterns, using a full stop at the end of a sentence is sometimes inappropriate. This research uses a deep learning approach to split each sentence from an Indonesian news document. Hence, there is no need to define any handcrafted features or rules. In Part of Speech Tagging and Named Entity Recognition, we use sequence labeling to determine sentence boundaries. Two labels will be used, namely O as a non-boundary token and E as the last token marker in the sentence. To do this, we used the Bi-LSTM approach, which has been widely used in sequence labeling. We have proved that our approach works for Indonesian text using pre-trained embedding in Indonesian, as in previous studies. This study achieved an F1-Score value of 98.49 percent. When compared to previous studies, the achieved performance represents a significant increase in outcomes..

scholarly journals A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings

2020 ◽  
Vol 34 (05) ◽  
pp. 9090-9097
Author(s):  
Niels Van der Heijden ◽  
Samira Abnar ◽  
Ekaterina Shutova
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Word Embeddings ◽  
Named Entity ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Joint Training ◽  
Comprehensive Comparison

The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP). Therefore, many recent studies focus on zero-shot transfer learning and joint training across languages to overcome data scarcity for low-resource languages. In this work we (i) perform a comprehensive comparison of state-of-the-art multilingual word and sentence encoders on the tasks of named entity recognition (NER) and part of speech (POS) tagging; and (ii) propose a new method for creating multilingual contextualized word embeddings, compare it to multiple baselines and show that it performs at or above state-of-the-art level in zero-shot transfer settings. Finally, we show that our method allows for better knowledge sharing across languages in a joint training setting.

scholarly journals Towards the Construction of a Gold Standard Biomedical Corpus for the Romanian Language

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 53 ◽  
Author(s):  
Maria Mitrofan ◽  
Verginica Barbu Mititelu ◽  
Grigorina Mitrofan
Keyword(s):  
Language Processing ◽  
Gold Standard ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Resources ◽  
Named Entities ◽  
Named Entity ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Biomedical Named Entity Recognition

Gold standard corpora (GSCs) are essential for the supervised training and evaluation of systems that perform natural language processing (NLP) tasks. Currently, most of the resources used in biomedical NLP tasks are mainly in English. Little effort has been reported for other languages including Romanian and, thus, access to such language resources is poor. In this paper, we present the construction of the first morphologically and terminologically annotated biomedical corpus of the Romanian language (MoNERo), meant to serve as a gold standard for biomedical part-of-speech (POS) tagging and biomedical named entity recognition (bioNER). It contains 14,012 tokens distributed in three medical subdomains: cardiology, diabetes and endocrinology, extracted from books, journals and blogposts. In order to automatically annotate the corpus with POS tags, we used a Romanian tag set which has 715 labels, while diseases, anatomy, procedures and chemicals and drugs labels were manually annotated for bioNER with a Cohen Kappa coefficient of 92.8% and revealed the occurrence of 1877 medical named entities. The automatic annotation of the corpus has been manually checked. The corpus is publicly available and can be used to facilitate the development of NLP algorithms for the Romanian language.

South China Sea Conflicts Classification Using Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Nur Rafeeqkha Sulaiman ◽  
Maheyzah Md Siraj
Keyword(s):  
South China Sea ◽  
Language Processing ◽  
South China ◽  
Named Entity Recognition ◽  
Online News ◽  
Entity Recognition ◽  
China Sea ◽  
Named Entity ◽  
Pos Tagging ◽  
Part Of Speech

Internet connects everyone to everything globally. The existence of Internet eases people in completing daily tasks. Thanks to Internet, information is being digitalized and spread openly to the public. Online news articles not only provide us with useful and reliable information and reports, it also eases information extraction and gathering for research purposes especially in Natural Language Processing (NLP) and Machine Learning (ML). The topics regarding the South China Sea have been popular lately due to the rise of conflicts between several countries claim on the islands in the sea. Gathering data through Internet and online sources proves to be easy, but to process a huge amount data and to identify only useful information manually takes a longer time to complete. Extracting important features from a text document can be done by using one or a combination of feature extraction methods. Relevant information and the classification of news articles in relation to the conflicts in South China Sea need to be done. In this paper, a model is proposed to use Named Entity Recognition (NER) that search for and classifies important information regarding to the conflicts. In order to do that, a combination of Part-of-Speech (POS) and NER are needed to extract type of conflicts from the news.  This study also claims to classify news by using Conditional Random Field (CRF) algorithm and Multinomial Naïve Bayes (MNB) as classification methods by training and testing the data. 

scholarly journals Bodo Resources for NLP - An Overview of Existing Primary Resources for Bodo

2021 ◽  
Author(s):  
Mwnthai Narzary ◽  
Gwmsrang Muchahary ◽  
Maharaj Brahma ◽  
Sanjib Narzary ◽  
Pranav Kumar Singh ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Parallel Corpus ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Challenges And Opportunities ◽  
Speech Tagging

With over 1.4 million Bodo speakers, there is a need for Automated Language Processing systems such as Machine translation, Part Of Speech tagging, Speech recognition, Named Entity Recognition, and so on. In order to develop such a system it requires a sufficient amount of dataset. In this paper we present a detailed description of the primary resources available for Bodo language that can be used as datasets to study Natural Language Processing and its applications. We have listed out different resources available for Bodo language: 8,005 Lexicon dataset collected from agriculture and health, Raw corpus dataset of 2,915,544 words, Tagged corpus consisting of 30,000 sentences, Parallel corpus of 28,359 sentences from tourism, agriculture and health and Tagged and Parallel corpus dataset of 37,768 sentences. We further discuss the challenges and opportunities present in Bodo language.

scholarly journals Learning Tag Dependencies for Sequence Tagging

2018 ◽  
Author(s):  
Yuan Zhang ◽  
Hongshen Chen ◽  
Yihong Zhao ◽  
Qun Liu ◽  
Dawei Yin
Keyword(s):  
Language Processing ◽  
Channel Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Proposed Model ◽  
Speech Tagging

Sequence tagging is the basis for multiple applications in natural language processing. Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. Sequence tagging actually possesses complex dependencies and interactions among the input tokens and the output tags. We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously. A tag LSTM is augmented to manage the output tag dependencies and word-tag interactions, while three mechanisms are presented to efficiently incorporate token context representation and tag dependency. Extensive experiments on part-of-speech tagging and named entity recognition tasks show that  the proposed model outperforms the BiLSTM-CRF baseline by effectively incorporating the tag dependency feature.

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.

Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition

2021 ◽  
pp. 1-12
Author(s):  
Yingwen Fu ◽  
Nankai Lin ◽  
Xiaotian Lin ◽  
Shengyi Jiang
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Neural Models ◽  
Performance Models ◽  
Named Entity ◽  
High Resource ◽  
Benchmark Datasets

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.

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