Unified Neural Lexical Analysis Via Two‐Stage Span Tagging

Dependency parsing, which is a fundamental task in Natural Language Processing (NLP), has attracted a lot of interest in recent years. In general, it is a module in an NLP pipeline together with word segmentation and Part-Of-Speech (POS) tagging in real Chinese NLP application. The NLP pipeline, which is a cascade system, will lead to error propagation for the parsing. This paper proposes a global discriminative re-ranking model using non-local features from word segmentation, POS tagging and dependency parsing to re-rank the parse trees produced by an N-best enhanced NLP pipeline. Experimental results indicate that the proposed model can improve the performance of dependency parsing as well as word segmentation and POS tagging in an NLP pipeline.

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

<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.

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

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.

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.

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.

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 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.

Position-aware self-attention based neural sequence labeling

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.

A kind of new words discovery method based on word segmentation result is presented in this paper. Word segmentation is an important part of many Chinese Natural language processing (NLP) tasks. Improving the accuracy of Chinese word segmentation is a matter of great concern. With the increasing number of web text, more and more Chinese NLP tasks need to use micro-blog, movie review and other web text. The content of web text changes very fast and often contains a large number of new words. It is an important factor affecting the accuracy of word segmentation that word segmentation tools can not identify these new words. One way to solve this problem is to discover new words in the text to use and add these new words to the dictionaries on which the word segmentation tool depends. The traditional method of new words discovery can only find the words that do not exist in word segmentation tool’s dictionary. But these words do not necessarily affect the result of the word segmentation. That is, the words may be correctly segmented even if they are not added to the word segmentation tool’s dictionary. To address this issue, we propose to build a collection of candidate new words based on segmentation result. All the new words discovered in this way segmented by the word segmentation tool by mistake. Adding these new words to the word segmentation tool’s dictionary can improve the accuracy of the word segmentation more than traditional methods. Experiments on the DouBan movie review dataset show that our method can get better new words to improve the accuracy on movie review sentiment classification.

The advances in deep learning have led to great achievements in many Natural Language Processing (NLP) tasks. With the nature of language, i.e., sequential data, most NLP tasks can be framed into the sequence learning framework, such as text generation. As one of the most important foundations for modern NLP techniques, autoregressive generation models have achieved dominant performance in a great deal of NLP tasks. Therefore, this thesis emphasizes improving the autoregressive generation model for different NLP tasks. While many tasks can naturally fit into the sequence learning framework, some of them, e.g., building discourse parsing tree, require sophisticated designs to fit into neural models. Therefore, this thesis firstly emphasizes a novel unified framework for discourse parsing, which builds a discourse tree in a top-down depth-first manner, and it frames the task as an autoregressive generation task with the goal of each step being the prediction of the node position given a piece of text. The proposed approach is proven effective with extensive empirical experiments. In addition, I extend the above framework by proposing a hierarchical decoder, which leverages the information from parents and siblings of the nodes that are currently processed. The proposed decoder utilizes the nature of the tree structure and further improves the experiment performance on both discourse parsing and dependency parsing tasks. On the other hand, the de facto strategies, i.e., cross entropy loss and teacher forcing, for training the autoregressive generation models have been shown problematic in certain aspects. For example, cross entropy loss, which is one of the widely leveraged training objective functions, often leads to text degeneration in text generation, and teacher forcing suffers from the exposure bias problem, where there exists a mismatch between the training and testing setup. For text degeneration, I introduce a class of diminishing attentions, which enforces the submodularity of the coverage calculated by cross attention in the sequence-to-sequence model. The proposed diminishing attentions achieve notable improvement on several neural text generation tasks, including text summarization, machine translation, and image paragraph generation. Further, I propose a novel training objective, ScaleGrad, to replace cross entropy, which significantly reduces the degeneration problem in different text generation tasks. In fact, ScaleGrad can be extended to problems beyond text degeneration. It provides wide flexibility to inject different inductive biases into the text generation model by directly modifying the gradient information in the output layer. Next, for the exposure bias problem, this thesis introduces a novel type of scheduled sampling based on training accuracy, which requires only minimal hyper-parameter tuning compared to existing scheduled sampling methods. Additionally, a novel imitation loss is proposed to further enforce the model’s generative behavior to match the teacher-forced behavior. Moreover, this thesis demonstrates that reducing exposure bias can improve the robustness of language models against repetition and toxic errors.

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.