Named Entity Recognition with Bidirectional LSTM-CNNs

Chiu, Jen-Fu; Nichols, Eric

doi:10.48550/arxiv.1511.08308

Cited by 19 publications

(27 citation statements)

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“…Based on previous researches, two of the most promising pre-trained models, ULMFiT [29] and BERT [30] with bidirectional LSTM layers [31], were used to determine the sentiment of the sentences. These models were fine-tuned separately on the training data provided by the organizers, the transliterated data combined with the training data, the translated data combined with the training data and the combination of all the three datasets.…”

Section: Methodsmentioning

confidence: 99%

IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages

Puranik¹,

Bharathi²,

B³

2021

Preprint

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Sentiment analysis of social media posts and comments for various marketing and emotional purposes is gaining recognition. With the increasing presence of code-mixed content in various native languages, there is a need for ardent research to produce promising results. This research paper bestows a tiny contribution to this research in the form of sentiment analysis of code-mixed social media comments in the popular Dravidian languages Kannada, Tamil and Malayalam. It describes the work for the shared task conducted by Dravidian-CodeMix at FIRE 2021 by employing pre-trained models like ULMFiT and multilingual BERT fine-tuned on the code-mixed dataset, transliteration (TRAI) of the same, English translations (TRAA) of the TRAI data and the combination of all the three. The results are recorded in this research paper where the best models stood 4th, 5th and 10th ranks in the Tamil, Kannada and Malayalam tasks respectively.

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Section: Methodsmentioning

confidence: 99%

IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages

Puranik¹,

Bharathi²,

B³

2021

Preprint

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“…• Name Entity Recognition (NER): The Name Entity Recognition algorithms identifies name entities(i.e., Person, location or organization) from a given text. We used the method described by Chiu et al [23] and used it to extract key concepts from a report. We split the data set into 80:10:10 ratio for train, validation and test set.…”

Section: Methodsmentioning

confidence: 99%

Pathology Extraction from Chest X-Ray Radiology Reports: A Performance Study

Mostafiz¹,

Ashraf²

2018

Preprint

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Extraction of relevant pathological terms from radiology reports is important for correct image label generation and disease population studies. In this letter we compare the performance of some known application program interface (APIs) for the task of thoracic abnormality extraction from radiology reports. We explored several medical domain specific annotation tools like Medical Text Indexer(MTI) with Non-MEDLINE and Mesh On Demand(MOD) options and generic Natural Language Understanding (NLU) API provided by the IBM cloud. Our results show that although MTI and MOD are intended for extracting medical terms, their performance is worst compared to generic extraction API like IBM NLU. Finally, we trained a DNN-based Named Entity Recognition (NER) model to extract the key concept words from radiology reports. Our model outperforms the medical specific and generic API performance by a large margin. Our results demonstrate the inadequacy of generic APIs for pathology extraction task and establishes the importance of domain specific model training for improved results. We hope that these results motivate the research community to release larger de-identified radiology reports corpus for building high accuracy machine learning models for the important task of pathology extraction.

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“…1) Named Entity Recognition: Named Entity Recognition (NER) aims to locate and categorize named entities in context into pre-defined categories such as the names of people and places. The application of deep neural networks in NER has been investigated by the employment of CNN [107] and RNN architectures [108], as well as hybrid bidirectional LSTM and CNN architectures [14]. NeuroNER [109], a named-entity recognition tool, operates based on artificial neural networks.…”

Section: Information Extractionmentioning

confidence: 99%

“…As a sequitur to remarkable progress achieved in adjacent disciplines utilizing deep learning methods, deep neural networks have been applied to various NLP tasks, including partof-speech tagging [10]- [12], named entity recognition [13], [13], [14], and semantic role labeling [15]- [17]. Most of the research efforts in deep learning associated with NLP applications involve either supervised learning 1 or unsupervised learning 2 .…”

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confidence: 99%

Natural Language Processing Advancements By Deep Learning: A Survey

Torfi,

Shirvani,

Keneshloo

et al. 2020

Preprint

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Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications, and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.

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Named Entity Recognition with Bidirectional LSTM-CNNs

Cited by 19 publications

References 0 publications

IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages

IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages

Pathology Extraction from Chest X-Ray Radiology Reports: A Performance Study

Natural Language Processing Advancements By Deep Learning: A Survey

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