scholarly journals IMPROVING CHINESE MEDICAL NAMED ENTITY RECOGNITION USING GLYPH AND LEXICON

2021 ◽  
Author(s):  
SHANHAO ZHONG ◽  
QINGSONG YU
Keyword(s):  
Semantic Representation ◽  
Experimental Studies ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entities ◽  
Natural Language Text ◽  
Named Entity ◽  
Structured Information ◽  
Text Information ◽  
Language Text

Abstract. Medical named entity recognition is the first step in processing electronic medical records. It is the basis for processing medical natural language text information into medical structured information, which has extremely high research value and application value. In this paper, we have proposed a model that aims to identify various types of named entities such as disease, imaging examination, laboratory examination, operation, drug, and anatomy from Chinese electronic medical record. We construct a fusion Glyph and lexicon model based on BERT. Experimental studies have shown that increasing character-level semantic representation can improve the performance of named entity recognition. In order to boost it, the major measures of our model include: (1) a CNN structure is proposed to capture glyph information. (2) Soft-Lexicon method is introduced to encode lexicon information. Our models show an improvement over the baseline BERT-BiLSTM-CRF model. The experimental results on CCKS2019 dataset showed that the F1 score was 84.64, which was +1.99 higher than the baseline level.

GENERATION OF A LARGE GENE/PROTEIN LEXICON BY MORPHOLOGICAL PATTERN ANALYSIS

2004 ◽  
Vol 01 (04) ◽  
pp. 611-626 ◽  
Author(s):  
LORRAINE TANABE ◽  
W. JOHN WILBUR
Keyword(s):  
Pattern Analysis ◽  
Inductive Logic ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Large Set ◽  
Morphological Pattern ◽  
Natural Language Text ◽  
Named Entity ◽  
Large Gene ◽  
Language Text

The identification of gene/protein names in natural language text is an important problem in named entity recognition. In previous work we have processed MEDLINE© documents to obtain a collection of over two million names of which we estimate that perhaps two thirds are valid gene/protein names. Our problem has been how to purify this set to obtain a high quality subset of gene/protein names. Here we describe an approach which is based on the generation of certain classes of names that are characterized by common morphological features. Within each class inductive logic programming (ILP) is applied to learn the characteristics of those names that are gene/protein names. The criteria learned in this manner are then applied to our large set of names. We generated 193 classes of names and ILP led to criteria defining a select subset of 1,240,462 names. A simple false positive filter was applied to remove 8% of this set leaving 1,145,913 names. Examination of a random sample from this gene/protein name lexicon suggests it is composed of 82% (±3%) complete and accurate gene/protein names, 12% names related to genes/proteins (too generic, a valid name plus additional text, part of a valid name, etc.), and 6% names unrelated to genes/proteins. The lexicon is freely available at .

scholarly journals Joint Posterior Revision of NLP Annotations via Ontological Knowledge

2018 ◽  
Author(s):  
Marco Rospocher ◽  
Francesco Corcoglioniti
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
World Knowledge ◽  
Entity Linking ◽  
Prior Probabilities ◽  
Natural Language Text ◽  
Named Entity ◽  
Ontological Entity ◽  
Language Text ◽  
Common World

Different well-established NLP tasks contribute to elicit the semantics of entities mentioned in natural language text, such as Named Entity Recognition and Classification (NERC) and Entity Linking (EL). However, combining the outcomes of these tasks may result in NLP annotations --- such as a NERC organization linked by EL to a person --- that are unlikely or contradictory when interpreted in the light of common world knowledge about the entities these annotations refer to. We thus propose a general probabilistic model that explicitly captures the relations between multiple NLP annotations for an entity mention, the ontological entity classes implied by those annotations, and the background ontological knowledge those classes may be consistent with. We use the model to estimate the posterior probability of NLP annotations given their confidences (prior probabilities) and the ontological knowledge, and consequently revise the best annotation choice performed by the NLP tools. In a concrete scenario with two state-of-the-art tools for NERC and EL, we experimentally show on three reference datasets that for these tasks, the joint annotation revision performed by the model consistently improves on the original results of the tools.

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 TEI-friendly annotation scheme for medieval named entities: a case on a Spanish medieval corpus

2021 ◽  
Author(s):  
Elena Álvarez-Mellado ◽  
María Luisa Díez-Platas ◽  
Pablo Ruiz-Fabo ◽  
Helena Bermúdez ◽  
Salvador Ros ◽  
...  
Keyword(s):  
Historical Data ◽  
Named Entity Recognition ◽  
Rich Source ◽  
Entity Recognition ◽  
Historical Evidence ◽  
Annotation Scheme ◽  
Named Entities ◽  
General Domain ◽  
Named Entity ◽  
Entity Annotation

AbstractMedieval documents are a rich source of historical data. Performing named-entity recognition (NER) on this genre of texts can provide us with valuable historical evidence. However, traditional NER categories and schemes are usually designed with modern documents in mind (i.e. journalistic text) and the general-domain NER annotation schemes fail to capture the nature of medieval entities. In this paper we explore the challenges of performing named-entity annotation on a corpus of Spanish medieval documents: we discuss the mismatches that arise when applying traditional NER categories to a corpus of Spanish medieval documents and we propose a novel humanist-friendly TEI-compliant annotation scheme and guidelines intended to capture the particular nature of medieval entities.

scholarly journals A Survey of Arabic Named Entity Recognition and Classification

2014 ◽  
Vol 40 (2) ◽  
pp. 469-510 ◽  
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.

scholarly journals Exploring the Adaptation of Recurrent Neural Network Approaches for Extracting Drug–Drug Interactions from Biomedical Text

2021 ◽  
Vol 11 (4) ◽  
pp. 267-273
Author(s):  
Wen-Juan Hou ◽  
◽  
Bamfa Ceesay
Keyword(s):  
Text Processing ◽  
Named Entity Recognition ◽  
Event Extraction ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Automatic Extraction ◽  
Named Entity ◽  
Structured Information ◽  
Network Approaches ◽  
Form Information

Information extraction (IE) is the process of automatically identifying structured information from unstructured or partially structured text. IE processes can involve several activities, such as named entity recognition, event extraction, relationship discovery, and document classification, with the overall goal of translating text into a more structured form. Information on the changes in the effect of a drug, when taken in combination with a second drug, is known as drug–drug interaction (DDI). DDIs can delay, decrease, or enhance absorption of drugs and thus decrease or increase their efficacy or cause adverse effects. Recent research trends have shown several adaptation of recurrent neural networks (RNNs) from text. In this study, we highlight significant challenges of using RNNs in biomedical text processing and propose automatic extraction of DDIs aiming at overcoming some challenges. Our results show that the system is competitive against other systems for the task of extracting DDIs.

scholarly journals NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Kanix Wang ◽  
Robert Stevens ◽  
Halima Alachram ◽  
Yu Li ◽  
Larisa Soldatova ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Analysis Tool ◽  
Automated Extraction ◽  
Named Entities ◽  
Named Entity ◽  
Automated Knowledge ◽  
Biomedical Texts ◽  
Machine Reading ◽  
Biomedical Named Entity Recognition

AbstractMachine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

scholarly journals A Probability based Classification of Named Entities for Malayalam Language combining Word, Part of Speech and Lexicalized features

2019 ◽  
Vol 8 (2) ◽  
pp. 839-842
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Supervised Machine Learning ◽  
Named Entities ◽  
Named Entity ◽  
Domain Specific ◽  
Part Of Speech ◽  
Classification Probability ◽  
Malayalam Language

Named Entity Recognition is the process wherein named entities which are designators of a sentence are identified. Designators of a sentence are domain specific. The proposed system identifies named entities in Malayalam language belonging to tourism domain which generally includes names of persons, places, organizations, dates etc. The system uses word, part of speech and lexicalized features to find the probability of a word belonging to a named entity category and to do the appropriate classification. Probability is calculated based on supervised machine learning using word and part of speech features present in a tagged training corpus and using certain rules applied based on lexicalized features.

scholarly journals Recursively Binary Modification Model for Nested Named Entity Recognition

2020 ◽  
Vol 34 (05) ◽  
pp. 8164-8171
Author(s):  
Bing Li ◽  
Shifeng Liu ◽  
Yifang Sun ◽  
Wei Wang ◽  
Xiang Zhao
Keyword(s):  
Strong Evidence ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Bayesian Framework ◽  
Entity Recognition ◽  
Named Entities ◽  
Named Entity ◽  
Nested Structures ◽  
Benchmark Datasets ◽  
Head Component

Recently, there has been an increasing interest in identifying named entities with nested structures. Existing models only make independent typing decisions on the entire entity span while ignoring strong modification relations between sub-entity types. In this paper, we present a novel Recursively Binary Modification model for nested named entity recognition. Our model utilizes the modification relations among sub-entities types to infer the head component on top of a Bayesian framework and uses entity head as a strong evidence to determine the type of the entity span. The process is recursive, allowing lower-level entities to help better model those on the outer-level. To the best of our knowledge, our work is the first effort that uses modification relation in nested NER task. Extensive experiments on four benchmark datasets demonstrate that our model outperforms state-of-the-art models in nested NER tasks, and delivers competitive results with state-of-the-art models in flat NER task, without relying on any extra annotations or NLP tools.

Named Entity Recognition and transliteration in Bengali

2007 ◽  
Vol 30 (1) ◽  
pp. 95-114 ◽  
Author(s):  
Asif Ekbal ◽  
Sudip Kumar Naskar ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Channel Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Linguistic Knowledge ◽  
Linguistic Features ◽  
Named Entities ◽  
Named Entity ◽  
The Third ◽  
News Corpus ◽  
Model C

The paper reports about the development of a Named Entity Recognition (NER) system in Bengali using a tagged Bengali news corpus and the subsequent transliteration of the recognized Bengali Named Entities (NEs) into English. Three different models of the NER have been developed. A semi-supervised learning method has been adopted to develop the first two models, one without linguistic features (Model A) and the other with linguistic features (Model B). The third one (Model C) is based on statistical Hidden Markov Model. A modified joint-source channel model has been used along with a number of alternatives to generate the English transliterations of Bengali NEs and vice-versa. The transliteration models learn the mappings from the bilingual training sets optionally guided by linguistic knowledge in the form of conjuncts and diphthongs in Bengali and their representations in English. The NER system has demonstrated the highest average Recall, Precision and F-Score values of 89.62%, 78.67% and 83.79% respectively in Model C. Evaluation of the proposed transliteration models demonstrated that the modified joint source-channel model performs best in terms of evaluation metrics for person and location names for both Bengali to English (B2E) transliteration and English to Bengali transliteration (E2B). The use of the linguistic knowledge during training of the transliteration models improves performance.

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