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Towards a Novel Weakly Supervised Joint Approach of Named Entity Recognition and Normalization for Noisy Text

Named Entity Recognition (NER) is a vital task in natural language processing (NLP) that involves identifying and classifying named entities in text. This paper focuses on the comparison of four different algorithms of NER techniques in the context of India. The four different algorithms are CRF (Conditional Random Fields), Stanza, Spacy, and Nameparser. The accuracy will be compared with the expected accuracy of all four methods of NER. Accuracy will be obtained after running the method on a dataset or text. To calculate the accuracy of four algorithms a large dataset of Indian names will be used. A comparison graph was obtained to display the accuracy graph of all four algorithms. "NLP-Named Entity Recognition in India" which focuses on detecting person names, states, and districts, and suggesting names based on religion. Named Entity Recognition (NER) is a Natural Language Processing (NLP) task that identifies and classifies named entities in text. Named entities are typically proper nouns, such as person names, organization names, and location names. NER is a challenging task for Indian languages, due to the diversity of Indian languages and the lack of resources for training NER systems. The proposed system can be used to improve the accuracy and efficiency of a variety of NLP applications in India.

Towards a Novel Weakly Supervised Joint Approach of Named Entity Recognition and Normalization for Noisy Text

Named Entity Recognition (NER) is a basic task in Natural Language Processing (NLP). Recently, the sequence-to-sequence (seq2seq) model has been widely used in NLP task. Different from the general NLP task, 60% sentences in the NER task do not contain entities. Traditional seq2seq method cannot address this issue effectively. To solve the aforementioned problem, we propose a novel seq2seq model, named SC-NER, for NER task. We construct a classifier between the encoder and decoder. In particular, the classifier’s input is the last hidden state of the encoder. Moreover, we present the restricted beam search to improve the performance of the proposed SC-NER. To evaluate our proposed model, we construct the patent documents corpus in the communications field, and conduct experiments on it. Experimental results show that our SC-NER model achieves better performance than other baseline methods.

Work orders, equipment information, technical records and best practices documents contain within them a wealth of insights related to Equipment Maintenance which can be unlocked with Natural Language Processing tasks like classification, clustering, named entity recognition or part of speech tagging. But obtaining large enough labelled data sets in Equipment Maintenance domain manually is prohibitive and very expensive. This lack of labeled Equipment Maintenance data can be overcome with Large Language Models (LLMs) such as GPT-3, BERT that are pretrained transformer networks, considered state-of-the-art when it comes to Natural Language Processing (NLP) tasks. However, the vocabulary understood by these LLMs are mostly from English Language and need to be fine-tuned to understand industry and organization specific vocabulary and acronyms. This paper explores the potential of a domain specific LLM model for oil and gas industries. Data that are of good quality and that provide a comprehensive overview of industry are collected. This corpus of text contains documents like work orders, equipment data and technical documents. A custom tokenizer is trained on this data to identify domain specific terminology. A comparative study is done with other off-the-shelf tokenizers: BERT and RoBERTa, to compare the effectiveness of the tokenization. With millions of work orders and equipment documents, training pipelines had to parallelized so that training can occur on multiple GPUs. A comprehensive study of multiple training methods is done in this paper. Model and tokenizer developed were packaged and archived to be consumed in machine learning pipelines to specific use-cases across the organization. For an organization adopting digital transformation, the availability of an organization specific LLM is an enabler to extract insights from millions of documents containing free text. The applicability of such models spans across multiple disciplines like Maintenance, Reliability, Safety etc. and streamlines the development of highly accurate and robust text analytics.

Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has significantly advanced Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), which involves identifying entities like person, location, and organization names in text. LLMs are especially promising for low-resource languages due to their ability to learn from limited data. However, the performance of GenAI models for Nepali, a low-resource language, has not been thoroughly evaluated. This paper investigates the application of state-of-the-art LLMs for Nepali NER, conducting experiments with various prompting techniques to assess their effectiveness. Our results provide insights into the challenges and opportunities of using LLMs for NER in low-resource settings and offer valuable contributions to the advancement of NLP research in languages like Nepali.

Named Entity Recognition (NER) is a challenging task in Natural Language Processing. Recently, machine learning based methods are widely used for the NER task and outperform traditional handcrafted rule based methods. As an alternative way to handle the NER task, stacking, which combines a set of classifiers into one classifier, has not been well explored for the NER task. In this paper, we propose a stacking model for the NER task. We extend the original stacking model from both model and feature aspects. We use Conditional Random Fields as the level-1 classifier, and we also apply meta-features from global aspect and local aspect of the level-0 classifiers and tokens in our model. In the experiments, our model achieves the state-of-the-art performance on the CoNLL 2003 Shared task.

<title>Abstract</title> The Tenyidie language, a.k.a Angami language, is a Tibeto-Burman Language of Nagaland in the north-eastern part of India. Tenyidie is a low-resource language spoken by the Tenyimia Community and is considered a major language in Nagaland. Tenyidie is a tonal, Subject-Object-Verb, and highly agglutinative language. Among the many Natural Language Processing (NLP) tasks, part-of-speech tagging is one of the primary tasks which is used in building many other NLP tasks, such as dependency parsing, named entity recognition, machine translation, etc. The main aim of this paper is to develop the Part-of-speech (POS) Tagset and Tagger for the Tenyidie Language. To the best of our knowledge, there is no reported work on POS Tagset and POS Tagger for the Tenyidie language. The main contributions of this research are: creation of POS tagset for the Tenyidielanguage, creation of POS annotated dataset (11,540 sentences), and the verification of the proposed POS tagset and annotated dataset using the Machine Learning/Deep Learning techniques such as CRF, BLSTM, etc., including the state-of-the-art model such as BERT.

This chapter presents a brief introduction to entropy guided transformation learning (ETL), a machine learning algorithm for classification tasks. ETL generalizes transformation based learning (TBL) by automatically solving the TBL bottleneck: the construction of good template sets. The main advantage of ETL is its easy applicability to natural language processing (NLP) tasks. This introductory chapter presents the motivation behind ETL and summarizes our experimental results. In Sect. 1.1, we first briefly detail TBL and explain its bottleneck. Next, we briefly present ETL and list some of its advantages. In Sect. 1.2, we first list some related works on the use of ETL for different NLP tasks. Next, we report a summary of our experimental results on the application of ETL to four language independent NLP tasks: part-of-speech tagging, phrase chunking, named entity recognition and semantic role labeling. Finally, in Sect. 1.3, we detail the structure of the book.KeywordsMachine learningEntropy guided transformation learningETL committeeTransformation based learningNatural language processingPart-of-speech taggingPhrase chunkingNamed entity recognitionSemantic role labeling

Named entity recognition (NER) is an important fundamental task in natural language processing (NLP). This paper describes a method for named entity recognition based on pretrained models and adversarial training in scientific text. The scientific entity recognition task requires the model to identify 7 different scientific term entities. There are some issues with a given dataset, such as imbalanced label classes, excessively long entity bounds, and inconsistent entity labeling. To address these issues, we proposed to use focal loss instead of existing cross-entropy loss. Further, we used one of the common adversarial training methods, i.e., Fast Gradient Method (FGM) to perform semi-supervised NER. The experimental results show that our adversarial training method considerably enhances the performance of the model, and the method used in this study achieves the highest F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score of 88.92%. Moreover, our results also prove that SciBERT is better suited to the task of named entity recognition in scientific text and that the focal loss successfully solves the problem of data imbalance.

Named Entity Recognition (NER), which aims to identify the span and category of entities within text, is a fundamental task in natural language processing. Recent NER approaches have featured pre-trained transformer-based models (e.g., BERT) as a crucial encoding component to achieve state-of-the-art performance. However, due to the length limit for input text, these models typically consider text at the sentence-level and cannot capture the long-range contextual dependency within a document. To address this issue, we propose a novel Span Graph Transformer (SGT) method for document-level NER, which constructs long-range contextual dependencies at both the token and span levels. Specifically, we first retrieve relevant contextual sentences in the document for each target sentence, and jointly encode them by BERT to capture token-level dependencies. Then, our proposed model extracts candidate spans from each sentence and integrates these spans into a document-level span graph, where nested spans within sentences and identical spans across sentences are connected. By leveraging the power of Graph Transformer and well-designed position encoding, our span graph can fully exploit span-level dependencies within the document. Extensive experiments on both resource-rich nested and flat NER datasets, as well as low-resource distantly supervised NER datasets, demonstrate that proposed SGT model achieves better performance than previous state-of-the-art models.

Named entity recognition (NER) is an important task in natural language processing, as it is widely featured as a key information extraction sub-task with numerous application areas. A plethora of attempts was made for NER detection in Western and Asian languages. However, little effort has been made to develop techniques for the Urdu language, which is a prominent South Asian language with hundreds of millions of speakers across the globe. NER in Urdu is considered a hard problem owing to several reasons, including the paucity of large, annotated datasets; an inaccurate tokenizer; and the absence of capitalization in the Urdu language. To this end, this study proposed a conditional-random-field-based technique with both language-dependent and language-independent features, such as part-of-speech tags and context windows of words, respectively. As a second contribution, we developed an Urdu NER dataset (UNER-I) in which a large number of NE types were manually annotated. To evaluate the effectiveness of the proposed approach, as well as the usefulness of the dataset, experiments were performed using the dataset we developed and an existing dataset. The results of the experiments showed that our proposed technique outperformed the baseline technique for both datasets by improving the F1 scores by 1.5% to 3%. Furthermore, the results demonstrated that the enhanced dataset was useful for learning and prediction in a supervised learning approach.

Text extraction is an important part of natural language processing (NLP) tasks. Most NLP tasks like text classification, machine translation, text-to-speech, text-based language identification, text summarization, and named-entity recognition involve the use of textual data. Such data is limited for low-resourced languages making it difficult to experiment advanced NLP techniques on these languages. This paper presents a Python-based toolkit for text analysis and text extraction from different types of images, documents, and audio files. The toolkit is built as a library that has functions that can be imported and utilized for text extraction.

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