On the Use of Parsing for Named Entity Recognition

Alonso, Miguel Ángel Verdugo; Gómez-Rodríguez, Carlos; Vilares, Jesús

doi:10.3390/app11031090

Cited by 9 publications

(6 citation statements)

References 113 publications

(122 reference statements)

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“…Named entity recognition (NER) 8 is a technique for extracting the words or expressions of a specific entity from unstructured text data. It was first defined by the message understanding conference (MUC) 9 into three categories (entity type, time type and number type) and seven subcategories (person name, institution name, place name, time, date, currency and percentage) 10 . Named entity recognition is an important foundation of natural language processing tasks 11 , such as information extraction, question answering systems, knowledge mapping and machine translation.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based methods for natural hazard named entity recognition

Sun

Liu

Cui

et al. 2022

Sci Rep

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Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide reference for natural hazard mitigation. The method of named entity recognition has many challenges, such as fast change, multiple types and various forms of named entities. This can introduce difficulties in research of natural hazard named entity recognition. To address the above problem, this paper constructed a natural disaster annotated corpus for training and evaluation model, and selected and compared several deep learning methods based on word vector features. A deep learning method for natural hazard named entity recognition can automatically mine text features and reduce the dependence on manual rules. This paper compares and analyzes the deep learning models from three aspects: pretraining, feature extraction and decoding. A natural hazard named entity recognition method based on deep learning is proposed, namely XLNet-BiLSTM-CRF model. Finally, the research hotspots of natural hazards papers in the past 10 years were obtained through this model. After training, the precision of the XLNet-BilSTM-CRF model is 92.80%, the recall rate is 91.74%, and the F1-score is 92.27%. The results show that this method, which is superior to other methods, can effectively recognize natural hazard named entities.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based methods for natural hazard named entity recognition

Sun

Liu

Cui

et al. 2022

Sci Rep

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“…These require more comprehension in machine reading; examples are machine translation, question-answering, reasoning, text summarization, and sentence condensation (Zeng et al 2020). Building block applications enhance the performance of the first two types of NLP applications (Taboada et al 2013), and common building blocks in NLP are part-of-speech (POS) tagging, named entity recognition (NER), and dependency parsing (Alonso, Gómez-Rodríguez, and Vilares 2021). Our discussion on text preprocessing methods in this article is built to serve a wide range of NLP applications.…”

Section: Nlp Application Typesmentioning

confidence: 99%

Comparison of text preprocessing methods

Chai

2022

Nat. Lang. Eng.

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Text preprocessing is not only an essential step to prepare the corpus for modeling but also a key area that directly affects the natural language processing (NLP) application results. For instance, precise tokenization increases the accuracy of part-of-speech (POS) tagging, and retaining multiword expressions improves reasoning and machine translation. The text corpus needs to be appropriately preprocessed before it is ready to serve as the input to computer models. The preprocessing requirements depend on both the nature of the corpus and the NLP application itself, that is, what researchers would like to achieve from analyzing the data. Conventional text preprocessing practices generally suffice, but there exist situations where the text preprocessing needs to be customized for better analysis results. Hence, we discuss the pros and cons of several common text preprocessing methods: removing formatting, tokenization, text normalization, handling punctuation, removing stopwords, stemming and lemmatization, n-gramming, and identifying multiword expressions. Then, we provide examples of text datasets which require special preprocessing and how previous researchers handled the challenge. We expect this article to be a starting guideline on how to select and fine-tune text preprocessing methods.

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“…The system was trained on a set of US general election presidential debates where each sentence was anotated as "Non-Factual", "Unimportant Factual", or "Check-worthy Factual". For each sentence, they extracted as features its sentiment polarity, its length, its bag of words, the number of ocurrences of every part of speech, and its named entities [181]. All of this resulted in a set of 6615 features, among which sentiment turned out to be the third most relevant one.…”

Section: Sa As a Feature For Fake News Detection Systemsmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

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In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

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On the Use of Parsing for Named Entity Recognition

Cited by 9 publications

References 113 publications

Deep learning-based methods for natural hazard named entity recognition

Deep learning-based methods for natural hazard named entity recognition

Comparison of text preprocessing methods

Sentiment Analysis for Fake News Detection

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