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检索条件"任意字段=7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024"

共 580 条记录，以下是71-80 订阅

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A Study Of Dialog Summarization Across Datasets And Domains 7

A Study Of Dialog Summarization Across Datasets And Domains

引用

7th international conference on natural language processing and Information Retrieval (NLPIR)

作者： Ranganathan, Aarthi Tamminaina, Sai Gowtham Raina, Gaurav Toyota Connected India Chennai India Indian Inst Technol Madras Chennai India

ISBN: (纸本)9798400709227

this study of dialog summarization covers multi-domain, multi-modal and multilingual datasets, and the potential challenges in the different domains. the scope and progress of this rapidly evolving topic rely on the availability of datasets and emerging domains. Such a study can facilitate the cross-application of datasets to different domains to refine models and also aid scenarios where there is a lack of data in privacy-sensitive settings. Further, our work can enable the cross-fertilization of ideas across domains and in different contexts. Our study encompasses current and emerging domains, a comprehensive compilation of datasets, and avenues for further research.

关键词： Dialog summarization Datasets Domains NLP machine learning

来源：评论

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Named Entity Recognition for Setswana language: A Conditional Random Fields (CRF) Approach 7

Named Entity Recognition for Setswana Language: A Conditiona...

引用

7th international conference on natural language processing and Information Retrieval (NLPIR)

作者： Okgetheng, Boago Malema, Gabofetswe Univ Botswana Dept Comp Sci Gaborone Botswana

ISBN: (纸本)9798400709227

Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP) focused on identifying entities like individuals, organizations, and locations within text. Locating these entities can present initial challenges, and subsequent classification can be equally daunting. this complexity is exemplified in Setswana, where shared naming between locations and personal names adds an extra layer of intricacy. this study introduces a Setswana NER approach, featuring a Setswana Regex Annotator (SERxA) for preliminary entity classification, followed by BRAT tool annotation. Employing the Conditional Random Fields (CRF) algorithm, we establish a supervised statistical machine learning NER model for Setswana. Evaluation using standard metrics on a held-out test set attains impressive F1-scores of 0.94 for person entities and 0.79 for location entities. Our findings underscore the viability of NER in Setswana and emphasize the necessity of nurturing NLP resources for less-resourced languages.

关键词： Location

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Discovering Chinese concept-in-corpus

Discovering Chinese concept-in-corpus

引用

7th international conference on machine learning and Cybernetics

作者： Chen, Jian-Chao Zheng, Qi-Lun Li, Zhao S China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9781424420957

Concept is the basic of knowledge. A concept consists of a connotation and an extension. the paper comes up with a concept of Concept-In-Corpus which is a special kind of formal concept, and presents a discovering algorithm called FCWFT (Filtering Concept-word Based on Feature-tree) which automatically mine the connotation and the extension for a Chinese Concept-In-Corpus from corpus in Chinese. Our work is the first one attempting to mine formal Concepts from free texts in the area of natural language processing. We test the algorithm with a large scale corpus. the result is encouraging.

关键词： natural language processing knowledge concept Concept-word Concept-IC feature-tree

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A Web-based Collaborative Evaluation Tool for Automatically Learned Relation Extraction Patterns 53

A Web-based Collaborative Evaluation Tool for Automatically ...

引用

53rd Annual Meeting of the Association-for-Computational-Linguistics (ACS) / 7th international Joint conference on natural language processing of the Asian-Federation-of-natural-language-processing (IJCNLP)

作者： Hennig, Leonhard Li, Hong Krause, Sebastian Xu, Feiyu Uszkoreit, Hans DFKI Language Technol Lab Berlin Germany

ISBN: (纸本)9781941643990

Patterns extracted from dependency parses of sentences are a major source of knowledge for most state-of-the-art relation extraction systems, but can be of low quality in distantly supervised settings. We present a linguistic annotation tool that allows human experts to analyze and categorize automatically learned patterns, and to identify common error classes. the annotations can be used to create datasets that enable machine learning approaches to pattern quality estimation. We also present an experimental pattern error analysis for three semantic relations, where we find that between 24% and 61% of the learned dependency patterns are defective due to preprocessing or parsing errors, or due to violations of the distant supervision assumption.

关键词： Error analysis

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Improvised Transformer Network for NER on Low Resource English-Hindi Code-Mixed language from Scratch 7

Improvised Transformer Network for NER on Low Resource Engli...

引用

7th international conference on Soft Computing and machine Intelligence (ISCMI)

作者： Bhowmick, Rajat Subhra Ganguli, Isha Sil, Jaya IIEST Dept CST Sibpur 711103 India

ISBN: (纸本)9781728175591

the use of Code-mixed languages in social media platforms is a common phenomenon, which poses a new set of challenges for understanding Code-mixed languages in the field of natural language processing (NLP). the implementation of state-of-the-art (SOTA) algorithms is difficult due to the scarcity of available resources. In the work, we focus on one of the primary NLP tasks namely Named Entity Recognition (NER) for English-Hindi Code-mixed language. We propose an improvised Transformer network that learns word and character embedding from scratch and beneficial in processing low resource Code-mixed languages. We use the only available Twitter NER corpus and obtained a slight improvement over SOTA. the proposed transformer network is a general model and in the future can be useful for training low resource NLP tasks from scratch.

关键词： Deep learning Transformer network NLP NER Code-mixed languages

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Legal Judgment Prediction for Canadian Appeal Cases 7

Legal Judgment Prediction for Canadian Appeal Cases

引用

7th international conference on Data Science and machine learning Applications (CDMA)

作者： Almuslim, Intisar Inkpen, Diana Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781665410144

Law is one of the knowledge domains that are most reliant on textual material. Nowadays, however, it is very difficult and time-consuming for legal professionals to read, understand, and analyze all the available documents, due to the vast volume of case law that is published every day. In this age of legal big data, and with the increased availability of legal text online, many researchers have given more focus to the development of legal intelligent systems and applications. these intelligent systems can provide great services and solve many problems in legal domain. Over the last years, researchers have focused on predicting judicial case outcomes using natural language processing (NLP) and machine learning (ML) methods over case documents. thus, Legal Judgment Prediction (LJP) is the task of automatically predicting the outcome of a court case given only the text of the case. To the best of our knowledge, no prior research with this intention has been conducted in English for appeal courts in Canada, as of 2021. the NLP application to legal judgments, that our proposed methodology focuses on, is to predict the outcomes of cases by looking only at the description of cases written by the court. Because appeal court decisions are often binary, as in accept or reject, the task is defined as a binary classification problem between 'Allow' and 'Dismiss'. this is the general approach in the literature as well. We employ various classification methods including classical classifiers, Deep learning (DL) models, and compare their performances. Our best results are obtained using DL models with accuracy values reaching 93.46% and F1-scores reaching 0.92, which are on par with the best results in the literature. through this study, we hope to establish the basis for future research on the legal system of Canada and offer a baseline for future work.

关键词： natural language processing Legal Judgment Prediction Text Classification

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Automated Mining Of Names Using Parallel Hindi-English Corpus 7

Automated Mining Of Names Using Parallel Hindi-English Corpu...

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7th Workshop on Asian language Resources, ALR 2009

作者： Mahesh, R. Sinha, K. Indian Institute of Technology Kanpur India

ISBN: (纸本)1932432566

machine transliteration has a number of applications in a variety of natural language processing related tasks such as machine translation, information retrieval and question-answering. For automated learning of machine transliteration, a large parallel corpus of names in two scripts is required. In this paper we present a simple yet powerful method for automatic mining of Hindi-English names from a parallel corpus. An average 93% precision and 85% recall is achieved in mining of proper names. the method works even with a small corpus. We compare our results with Giza++ word alignment tool that yields 30% precision and 63% recall on the same corpora. We also demonstrate that this very method of name mining works for other Indian languages as well. © 2009 ACL and AFNLP

关键词： natural language processing systems

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Multi-Modal learning with Joint Image-Text Embeddings and Decoder Networks 7

Multi-Modal Learning with Joint Image-Text Embeddings and De...

引用

IEEE 7th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Chemmanam, Ajai John Jose, Bijoy A. Moopan, Asif Cochin Univ Sci & Technol CPS Lab Dept Elect Cochin Kerala India Vuelogix Technol Pvt Ltd Kochi Kerala India

ISBN: (纸本)9798350363029;9798350363012

Advances in machine learning and neural networks have transformed natural language processing (NLP) and computer vision (CV) applications. Recent research efforts have begun to bridge the gap between the two domains. In this work, we propose a semi supervised Multi-Modal Encoder Decoder Network (MMEDN) to capture the relationship between images and textual descriptions, allowing us to generate meaningful descriptions of images and retrieve images from a database using cross-modality search. the semi-supervised training approach, which combines ground truth text descriptions and pseudotext generated by the text decoder within the model, requires far fewer image-text pairs in the training data and can directly add new raw images without manual text labelling for training. this approach is particularly useful for active learning environments, where labels are expensive and hard to obtain. We show that our model performs well with qualitative evaluations. We applied our model for finding images of a person from large databases and generating descriptions of people involved in an event for adding to an automatically generated report. the model was able to retrieve relevant images and generate accurate descriptions, demonstrating its applicability to more practical use cases.

关键词： Multi-modal learning Cross-modal retrieval Encoder-decoder architectures Computer Vision natural language processing

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Tutorial on Fair and Private Deep learning 24

Tutorial on Fair and Private Deep Learning

引用

7th ACM India Joint international conference on Data Science and Management of Data (CODS-COMAD) / 11th ACM IKDD CODS conference / 29th COMAD conference

作者： Padala, Manisha Damle, Sankarshan Gujar, Sujit IISc Dept CSA Bangalore Karnataka India IIIT Machine Learning Lab Hyderabad India

ISBN: (纸本)9798400716348

Deep learning (DL) finds application in several prominent fields, including computer vision, natural language processing, and bioinformatics. the proliferation of DL-based methods has brought to notice critical issues about bias (or unfairness) in classification and weak privacy guarantees of the training data. It is crucial to prioritize addressing these issues to prevent the potentially significant negative impact on users. While there has been progress, majority of the works focus on independently resolving fairness and privacy. We propose a tutorial on "Fair and Private Deep learning" - aimed to provide an exhaustive discussion on (i) reasons behind unfair classifications and lack of privacy, (ii) fairness notions in literature and methods to ensure them, (iii) differentially private DL, and (iv) algorithms that address fair and private DL simultaneously. Moreover, in this tutorial, we not just limit our attention to classical, centralized DL models but also to the fairness and privacy challenges in distributed (or federated) DL. the code, presentation and other details are available at https://***/magnetar-iiith/FairPrivateDL.

关键词： Deep learning Federated learning Group Fairness Differential Privacy

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machine learning in Causal Inference

Machine Learning in Causal Inference

引用

machine learning and natural language processing (mlnlp), international conference on

作者： Yiwei Shen Commercial Aircraft Corporation of China Ltd. Shanghai China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Causal inference enables us to move beyond merely observing correlations in understanding the actual causal relationships between variables, but how to connect it with machine learning model still needs careful and scientific study to judge. this paper discusses various methods for estimating causal effects and their application in different scientific implementation. the feasibility of these methods is explored through data of a social research, Early Childhood Longitudinal Study (ECLS), illustrating findings out of traditional and machine learning procedure, to provide heterogeneous influence on estimating causal inference.

关键词： Correlation machine learning natural language processing

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