scholarly journals Tagging Named Entities in Croatian Tweets

2017 ◽  
Vol 4 (1) ◽  
pp. 20-41
Author(s):  
Krešimir Baksa ◽  
Dino Golović ◽  
Goran Glavaš ◽  
Jan Šnajder
Keyword(s):  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Named Entities ◽  
News Stories ◽  
Named Entity ◽  
Set Size ◽  
Named Entity Extraction ◽  
Textual Content

Named entity extraction tools designed for recognizing named entities in texts written in standard language (e.g., news stories or legal texts) have been shown to be inadequate for user-generated textual content (e.g., tweets, forum posts). In this work, we propose a supervised approach to named entity recognition and classification for Croatian tweets. We compare two sequence labelling models: a hidden Markov model (HMM) and conditional random fields (CRF). Our experiments reveal that CRF is the best model for the task, achieving a very good performance of over 87% micro-averaged F1 score. We analyse the contributions of different feature groups and influence of the training set size on the performance of the CRF model.

An Experimental Study of Hybrid Machine Learning Models for Extracting Named Entities

10.29007/dp5m ◽  
2019 ◽  
Author(s):  
Lei Jiang ◽  
Elena Bolshakova
Keyword(s):  
Neural Network ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Network Models ◽  
Entity Recognition ◽  
Neural Network Models ◽  
Named Entities ◽  
Hybrid Neural Network ◽  
Named Entity ◽  
Two Hybrid

The paper describes two hybrid neural network models for named entity recognition (NER) in texts, as well as results of experiments with them. The first model, namely Bi-LSTM-CRF, is known and used for NER, while the other model named Gated-CNN- CRF is proposed in this work. It combines convolutional neural network (CNN), gated linear units, and conditional random fields (CRF). Both models were tested for NER on three different language datasets, for English, Russian, and Chinese. All resulted scores of precision, recall and F1-measure for both models are close to the state-of-the-art for NER, and for the English dataset CoNLL-2003, Gated-CNN-CRF model achieves 92.66 of F1-measure, outperforming the known result.

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

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

scholarly journals Myanmar named entity corpus and its use in syllable-based neural named entity recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 1544 ◽  
Author(s):  
Hsu Myat Mo ◽  
Khin Mar Soe
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Future Research ◽  
Network Architectures ◽  
Entity Extraction ◽  
Named Entity ◽  
Named Entity Extraction ◽  
Network Approaches

Myanmar language is a low-resource language and this is one of the main reasons why Myanmar Natural Language Processing lagged behind compared to other languages. Currently, there is no publicly available named entity corpus for Myanmar language. As part of this work, a very first manually annotated Named Entity tagged corpus for Myanmar language was developed and proposed to support the evaluation of named entity extraction. At present, our named entity corpus contains approximately 170,000 name entities and 60,000 sentences. This work also contributes the first evaluation of various deep neural network architectures on Myanmar Named Entity Recognition. Experimental results of the 10-fold cross validation revealed that syllable-based neural sequence models without additional feature engineering can give better results compared to baseline CRF model. This work also aims to discover the effectiveness of neural network approaches to textual processing for Myanmar language as well as to promote future research works on this understudied language.

scholarly journals Conditional Random Fields for Biomedical Named Entity Recognition Revisited

2020 ◽  
Author(s):  
Xie-Yuan Xie
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biomedical Domain ◽  
Minimal Set ◽  
Named Entities ◽  
Named Entity ◽  
Biomedical Texts ◽  
Biomedical Named Entity Recognition

Abstract Named Entity Recognition (NER) is a key task which automatically extracts Named Entities (NE) from the text. Names of persons, places, date and time are examples of NEs. We are applying Conditional Random Fields (CRFs) for NER in biomedical domain. Examples of NEs in biomedical texts are gene, proteins. We used a minimal set of features to train CRF algorithm and obtained a good results for biomedical texts.

Tibetan-Chinese Named Entity Extraction Based on Comparable Corpus

2014 ◽  
Vol 571-572 ◽  
pp. 1202-1205
Author(s):  
Yuan Sun ◽  
Qian Zhao
Keyword(s):  
Extraction Method ◽  
Named Entity Recognition ◽  
Sentence Length ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Named Entity ◽  
Named Entity Extraction ◽  
Cross Language Information Retrieval ◽  
Cross Language ◽  
Intersection Method

Tibetan-Chinese named entity extraction is the foundation of cross language information processing, and provides a basis for machine translation and cross language information retrieval research. In this paper, we use the multi-language links of Wikipedia to obtain Tibetan-Chinese comparable corpus, and combine sentence length, word matching and entity boundary words together to get parallel sentence. Then we extract Tibetan-Chinese named entity from the comparable corpus in three ways: (1) Extracting Natural labeling information. (2) Acquiring the links of Tibetan entries and Chinese entries. (3) Using sequence intersection method, which includes the sentence representation, Chinese named entity recognition and corresponding Tibetan sentences intersection. Finally, the results show the extraction method based on comparable corpus is effective.

scholarly journals Automatic Arabic Named Entity Extraction and Classification for Information Retrieval

2020 ◽  
Vol 9 (6) ◽  
pp. 1-22
Author(s):  
Omar ASBAYOU
Keyword(s):  
Information Retrieval ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Rule Based ◽  
Named Entity ◽  
Named Entity Extraction ◽  
Name Entity ◽  
System Output

This article tries to explain our rule-based Arabic Named Entity recognition (NER) and classification system. It is based on lists of classified proper names (PN) and particularly on syntactico-semantic patterns resulting in fine classification of Arabic NE. These patterns use syntactico-semantic combination of morpho-syntactic and syntactic entities. It also uses lexical classification of trigger words and NE extensions. These linguistic data are essential not only to name entity extraction but also to the taxonomic classification and to determining the NE frontiers. Our method is also based on the contextualisation and on the notion of NE class attributes and values. Inspired from X-bar theory and immediate constituents, we built a rule-based NER system composed of five levels of syntactico-semantic combination. We also show how the fine NE annotations in our system output (XML database) is exploited in information retrieval and information extraction.

Machine-learning methods for text named entity recognition

2016 ◽  
pp. 150-157
Author(s):  
O.O. Marchenko ◽  
Keyword(s):  
Machine Learning ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Methods ◽  
Named Entities ◽  
Named Entity ◽  
Machine Learning Methods ◽  
Multi Classification

The article describes machine learning methods for the named entity recognition. To build named entity classifiers two basic models of machine learning, The Naїve Bayes and Conditional Random Fields, were used. A model for multi-classification of named entities using Error Correcting Output Codes was also researched. The paper describes a method for classifiers' training and the results of test experiments. Conditional Random Fields overcome other models in precision and recall evaluations.

scholarly journals Named-Entity Recognition in Sports Field Based on a Character-Level Graph Convolutional Network

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 30
Author(s):  
Xieraili Seti ◽  
Aishan Wumaier ◽  
Turgen Yibulayin ◽  
Diliyaer Paerhati ◽  
Lulu Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Conditional Random Fields ◽  
Structural Information ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Convolutional Network ◽  
Named Entities ◽  
Attention Model ◽  
Named Entity

Traditional methods for identifying naming ignore the correlation between named entities and lose hierarchical structural information between the named entities in a given text. Although traditional named-entity methods are effective for conventional datasets that have simple structures, they are not as effective for sports texts. This paper proposes a Chinese sports text named-entity recognition method based on a character graph convolutional neural network (Char GCN) with a self-attention mechanism model. In this method, each Chinese character in the sports text is regarded as a node. The edge between the nodes is constructed using a similar character position and the character feature of the named-entity in the sports text. The internal structural information of the entity is extracted using a character map convolutional neural network. The hierarchical semantic information of the sports text is captured by the self-attention model to enhance the relationship between the named entities and capture the relevance and dependency between the characters. The conditional random fields classification function can accurately identify the named entities in the Chinese sports text. The results conducted on four datasets demonstrate that the proposed method improves the F-Score values significantly to 92.51%, 91.91%, 93.98%, and 95.01%, respectively, in comparison to the traditional naming methods.

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.

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