Named Entity Recognition And Classification Research Articles

An evaluation of NERC learning-based approaches to discover personal data in Brazilian Portuguese documents

Governments are issuing regulations and laws demanding that companies protect collected and processed personal data. In Brazil, the federal government sanctioned the General Personal Data Protection law, which defines personal and sensitive data associated with Brazilian citizens. One existing barrier for companies to comply with the law is identifying where personal data is stored inside their infrastructure, mainly concerning personal data inserted into unstructured documents. Named Entity Recognition and Classification (NERC) can support companies in this task by implementing supervised learning models to identify personal data. In this study, we designed an experiment to evaluate machine learning-based NERC using BERT and LSTM approaches to recognize personal data related to Brazil’s context. We established a generic training corpus based on online documents and trained two models for each approach, one considering the original corpus and another after lowercasing it. The study also assessed relation extraction to differentiate personal entities from others. For NERC and relation extraction evaluations, we established a training corpus comprising documents from two organizations related to the education and health sectors. BERT fine-tuned with uncased corpus scored an F1 measure of 0.8 and achieved the best performance in recognizing entities, followed by LSTM based on the same corpus. After applying relation extraction, BERT models achieved better F1 scores than LSTM models. The uncased BERT model achieved an F1 score of 0.85, which was the best. Experiment results also indicated that relation extraction improves the performance of BERT models to discover personal entities.

Knowledge-Driven Joint Posterior Revision of Named Entity Classification and Linking

In this paper we address the problem of extracting quality entity knowledge from natural language text, an important task for the automatic construction of knowledge graphs from unstructured content. More in details, we investigate the benefit of performing a joint posterior revision, driven by ontological background knowledge, of the annotations resulting from natural language processing (NLP) entity analyses such as named entity recognition and classification (NERC) and entity linking (EL). The revision is performed via a probabilistic model, called JPARK, that given the candidate annotations independently identified by NERC and EL tools on the same textual entity mention, reconsiders the best annotation choice performed by the tools in light of the coherence of the candidate annotations with the ontological knowledge. The model can be explicitly instructed to handle the information that an entity can potentially be NIL (i.e., lacking a corresponding referent in the target linking knowledge base), exploiting it for predicting the best NERC and EL annotation combination. We present a comprehensive evaluation of JPARK along various dimensions, comparing its performances with and without exploiting NIL information, as well as the usage of three different background knowledge resources (YAGO, DBpedia, and Wikidata) to build the model. The evaluation, conducted using different tools (the popular Stanford NER and DBpedia Spotlight, as well as the more recent Flair NER and End-to-End Neural EL) with three reference datasets (AIDA, MEANTIME, and TAC-KBP), empirically confirms the capability of the model to improve the quality of the annotations of the given tools, and thus their performances on the tasks they are designed for.

Knowledge-driven joint posterior revision of named entity classification and linking

In this work we address the problem of extracting quality entity knowledge from natural language text, an important task for the automatic construction of knowledge graphs from unstructured content.More in details, we investigate the benefit of performing a joint posterior revision, driven by ontological background knowledge, of the annotations resulting from natural language processing (NLP) entity analyses such as named entity recognition and classification (NERC) and entity linking (EL). The revision is performed via a probabilistic model, called jpark, that given the candidate annotations independently identified by NERC and EL tools on the same textual entity mention, reconsiders the best annotation choice performed by the tools in light of the coherence of the candidate annotations with the ontological knowledge. The model can be explicitly instructed to handle the information that an entity can potentially be NIL (i.e., lacking a corresponding referent in the target linking knowledge base), exploiting it for predicting the best NERC and EL annotation combination.We present a comprehensive evaluation of jpark along various dimensions, comparing its performances with and without exploiting NIL information, as well as the usage of three different background knowledge resources (YAGO, DBpedia, and Wikidata) to build the model. The evaluation, conducted using different tools (the popular Stanford NER and DBpedia Spotlight, as well as the more recent Flair NER and End-to-End Neural EL) with three reference datasets (AIDA, MEANTIME, and TAC-KBP), empirically confirms the capability of the model to improve the quality of the annotations of the given tools, and thus their performances on the tasks they are designed for.

Urdu Named Entity Recognition and Classification System Using Artificial Neural Network

Named Entity Recognition and Classification (NERC) is a process of identifying words and classifying them into person names, location names, organization names, and so on. In this article, we discuss the development of an Urdu Named Entity (NE) corpus, called the Kamran-PU-NE (KPU-NE) corpus, for three entity types, that is, Person, Organization, and Location, and marking the remaining tokens as Others (O). We use two supervised learning algorithms, Hidden Markov Model (HMM) and Artificial Neural Network (ANN), for the development of the Urdu NERC system. We annotate the 652852-token corpus taken from 15 different genres with a total of 44480 NEs. The inter-annotator agreement between the two annotators in terms of Kappa k statistic is 73.41%. With HMM, the highest recorded precision, recall, and f-measure values are 55.98%, 83.11%, and 66.90%, respectively, and with ANN, they are 81.05%, 87.54%, and 84.17%, respectively.

Kannada Named Entity Recognition and Classification using Support Vector Machine

Named Entity Recognition and Classification (NERC) is a process of identification of proper nouns in the text and classification of those nouns into certain predefined categories like person name, location, organization, date, time etc. Kannada NERC is an essential and challenging work which aims at developing a novel model based on Support Vector Machine. In this paper, tf-idf and POS features are used, which are extracted from a training corpus created manually. Furthermore, the model is trained and tested with different kernels: polynomial, rbf, sigmoid and linear kernels. The details of implementation and performance evaluation are discussed. The experiments are conducted on a training corpus of size 1, 51,440 tokens and test corpus of 7,000, 11,000, 15,000, 20,000, 30,000, 40,000 and 50,000 tokens. It is observed that the model works with an average precision, recall and F1-measure of 87%, 88% and 87.5% respectively for a linear kernel SVM on the test corpus of 7,000 tokens.

Word Embedding 자질을 이용한 한국어 개체명 인식 및 분류

Named Entity Recognition and Classification (NERC) is a task for recognition and classification of named entities such as a person's name, location, and organization. There have been various studies carried out on Korean NERC, but they have some problems, for example lacking some features as compared with English NERC. In this paper, we propose a method that uses word embedding as features for Korean NERC. We generate a word vector using a Continuous-Bag-of-Word (CBOW) model from POS-tagged corpus, and a word cluster symbol using a K-means algorithm from a word vector. We use the word vector and word cluster symbol as word embedding features in Conditional Random Fields (CRFs). From the result of the experiment, performance improved 1.17%, 0.61% and 1.19% respectively for TV domain, Sports domain and IT domain over the baseline system. Showing better performance than other NERC systems, we demonstrate the effectiveness and efficiency of the proposed method.

Kannada Named Entity Recognition and Classification (NERC) Based on Multinomial Naïve Bayes (MNB) Classifier

Named Entity Recognition and Classification (NERC) is a process of identification of proper nouns in the text and classification of those nouns into certain predefined categories like person name, location, organization, date, and time etc. NERC in Kannada is an essential and challenging task. The aim of this work is to develop a novel model for NERC, based on Multinomial Na\"ive Bayes (MNB) Classifier. The Methodology adopted in this paper is based on feature extraction of training corpus, by using term frequency, inverse document frequency and fitting them to a tf-idf-vectorizer. The paper discusses the various issues in developing the proposed model. The details of implementation and performance evaluation are discussed. The experiments are conducted on a training corpus of size 95,170 tokens and test corpus of 5,000 tokens. It is observed that the model works with Precision, Recall and F1-measure of 83%, 79% and 81% respectively.

Named entity recognition and classification in biomedical text using classifier ensemble.

Named Entity Recognition and Classification (NERC) is an important task in information extraction for biomedicine domain. Biomedical Named Entities include mentions of proteins, genes, DNA, RNA, etc. which, in general, have complex structures and are difficult to recognise. In this paper, we propose a Single Objective Optimisation based classifier ensemble technique using the search capability of Genetic Algorithm (GA) for NERC in biomedical texts. Here, GA is used to quantify the amount of voting for each class in each classifier. We use diverse classification methods like Conditional Random Field and Support Vector Machine to build a number of models depending upon the various representations of the set of features and/or feature templates. The proposed technique is evaluated with two benchmark datasets, namely JNLPBA 2004 and GENETAG. Experiments yield the overall F- measure values of 75.97% and 95.90%, respectively. Comparisons with the existing systems show that our proposed system achieves state-of-the-art performance.

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

Named Entity Recognition and Classification (NERC) is an important component of applications like Opinion Tracking, Information Extraction, or Question Answering. When these applications require to work in several languages, NERC becomes a bottleneck because its development requires language-specific tools and resources like lists of names or annotated corpora. This paper presents a lightly supervised system that acquires lists of names and linguistic patterns from large raw text collections in western languages and starting with only a few seeds per class selected by a human expert. Experiments have been carried out with English and Spanish news collections and with the Spanish Wikipedia. Evaluation of NE classification on standard datasets shows that NE lists achieve high precision and reveals that contextual patterns increase recall significantly. Therefore, it would be helpful for applications where annotated NERC data are not available such as those that have to deal with several western languages or information from different domains.

Multiobjective Optimization for Biomedical Named Entity Recognition and Classification

Named Entity Recognition and Classification (NERC) is one of the most fundamental and important tasks in biomedical informa–tion extraction. Biomedical named entities (NEs) include mentions of proteins, genes, DNA, RNA etc. which, in general, have complex structures and are difficult to recognize. We have developed a large number of features for identifying NEs from biomed–ical texts. Two robust diverse classification methods like Conditional Random Field (CRF) and Support Vector Machine (SVM) are used to build a number of models depending upon the various representations of the set of features and/or feature templates. Finally the outputs of these different classifiers are combined using multiobjective weighted voted approach. We hypothesize that the reliability of predictions of each classifier differs among the various output classes. Thus, in an ensemble system, it is neces–sary to determine the appropriate weight of vote for each output class in each classifier. Here, a multiobjective genetic algorithm is utilized for determining appropriate weights of votes for combining the outputs of classifiers. The developed technique is evaluated with the benchmark dataset of JNLPBA 2004 that yields the overall recall, precision and F-measure values of 74.10%, 77.58% and 75.80%, respectively.

A survey of named entity recognition and classification

This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of errors, are an indisputable key to progress.

Cross-lingual Named Entity Recognition

Named Entity Recognition and Classification (NERC) is a known and well-explored text analysis application that has been applied to various languages. We are presenting an automatic, highly multilingual news analysis system that fully integrates NERC for locations, persons and organisations with document clustering, multi-label categorisation, name attribute extraction, name variant merging and the calculation of social networks. The proposed application goes beyond the state-of-the-art by automatically merging the information found in news written in ten different languages, and by using the aggregated name information to automatically link related news documents across languages for all 45 language pair combinations. While state-of-the-art approaches for cross-lingual name variant merging and document similarity calculation require bilingual resources, the methods proposed here are mostly language-independent and require a minimal amount of monolingual language-specific effort. The development of resources for additional languages is therefore kept to a minimum and new languages can be plugged into the system effortlessly. The presented online news analysis application is fully functional and has, at the end of the year 2006, reached average usage statistics of 600,000 hits per day.

Mining Linguistic Information Into An E-retail System

Building more adaptive SW applications is a crucial issue to scale up Information Technology to the Web, where information is organized following different underlying knowledge and/or presentation models. To efficiently manage heterogeneous information sources agents must be able to cooperate, share their knowledge, and agree upon appropriate terminology to be used during interaction. We describe here an e-retail product comparison agent system aiming to supply users with synthesis information on product fitting at best their inquires. We will focus on the Named Entity Recognition and Classification (NERC) component that is very helpfid in identifying relevant characteristics in a (multilingual) product description.