Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval最新文献_第4页

Dependency Graphs for Summarization and Keyphrase Extraction: We present a real-time long document summarization and key-phrase extraction algorithm that utilizes a unified dependency graph. 摘要和关键字提取的依赖图:我们提出了一种利用统一依赖图的实时长文档摘要和关键字提取算法。

Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval Pub Date : 2022-12-16 DOI: 10.1145/3582768.3582792

Yifan Guo, David Brock, Alicia Lin, Tam Doan, Ali Khan, Paul Tarau

引用次数: 0

Classification of advertisement articles using sentiment analysis: (Research-based on Korean natural language processing and deep learning technology) 基于情感分析的广告文章分类:(基于韩语自然语言处理和深度学习技术的研究)

Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval Pub Date : 2022-12-16 DOI: 10.1145/3582768.3582800

Yongjun Kim, Y. Byun

{"title":"Classification of advertisement articles using sentiment analysis: (Research-based on Korean natural language processing and deep learning technology)","authors":"Yongjun Kim, Y. Byun","doi":"10.1145/3582768.3582800","DOIUrl":"https://doi.org/10.1145/3582768.3582800","url":null,"abstract":"We live in a flood of big data and information through computers, communications, social media, and mass media. In other words, we can get the information we want quickly and easily, but we have many questions about the accuracy and reliability of this information. That is, there are many problems in trying to obtain accurate knowledge of such reckless details, and in particular, advertisement articles provided by online newspapers need to be clearer and more manageable when individuals try to find precise information and reports. Such experiences are threatened even to the foundation of existence due to distrust of Internet newspapers and advertisement evasion. To solve this problem, this study used emotion analysis of natural language processing to classify general and advertisement articles. Getting going Existing similar studies have mainly been undertaken to classify such advertisement articles, such as spam mail classification, and most of these studies used general natural language processing. However, this paper is a study that analyzes text data to understand further the meaning of the words, sentences, and phrases and adds steps to explore emotions to provide more accurate information that individuals want.","PeriodicalId":315721,"journal":{"name":"Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126939010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extraction of Common Physical Properties of Everyday Objects from Structured Sources 从结构化资源中提取日常对象的共同物理属性

Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval Pub Date : 2022-12-16 DOI: 10.1145/3582768.3582772

Viktor Losing, J. Eggert

引用次数: 1

Vietnamese Text Summarization Based on Elementary Discourse Units 基于初级语篇单元的越南语文本摘要

Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval Pub Date : 2022-12-16 DOI: 10.1145/3582768.3582793

Khang Nhut Lam, Tai Ngoc Nguyen, J. Kalita

引用次数: 0

A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language Processing 基于自然语言处理的否定检测和消歧的语义方法

Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval Pub Date : 2022-12-16 DOI: 10.1145/3582768.3582789

Izunna Okpala, Guillermo Romera Rodriguez, Andrea Tapia, S. Halse, Jessica Kropczynski

{"title":"A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language Processing","authors":"Izunna Okpala, Guillermo Romera Rodriguez, Andrea Tapia, S. Halse, Jessica Kropczynski","doi":"10.1145/3582768.3582789","DOIUrl":"https://doi.org/10.1145/3582768.3582789","url":null,"abstract":"This study aims to demonstrate the methods for detecting negations in a sentence by uniquely evaluating the lexical structure of the text via word-sense disambiguation. The proposed framework examines all the unique features in the various expressions within a text to resolve the contextual usage of all tokens and decipher the effect of negation on sentiment analysis. The application of popular expression detectors skips this important step, thereby neglecting the root words caught in the web of negation and making text classification difficult for machine learning and sentiment analysis. This study adopts the Natural Language Processing (NLP) approach to discover and antonimize words that were negated for better accuracy in text classification using a knowledge base provided by an NLP library called WordHoard. Early results show that our initial analysis improved on traditional sentiment analysis, which sometimes neglects negations or assigns an inverse polarity score. The SentiWordNet analyzer was improved by 35%, the Vader analyzer by 20% and the TextBlob by 6%.","PeriodicalId":315721,"journal":{"name":"Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122952877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Preventing RNN from Using Sequence Length as a Feature 防止RNN使用序列长度作为特征

Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval Pub Date : 2022-12-16 DOI: 10.1145/3582768.3582776

Jean-Thomas Baillargeon, Hélène Cossette, Luc Lamontagne

引用次数: 1

Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches 评估无监督文本分类:零概率和基于相似性的方法

Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval Pub Date : 2022-11-29 DOI: 10.1145/3582768.3582795

Tim Schopf, Daniel Braun, F. Matthes

{"title":"Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches","authors":"Tim Schopf, Daniel Braun, F. Matthes","doi":"10.1145/3582768.3582795","DOIUrl":"https://doi.org/10.1145/3582768.3582795","url":null,"abstract":"Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to generalize knowledge gained from a training task by assigning appropriate labels of unknown classes to text documents. Although existing studies have already investigated individual approaches to these categories, the experiments in literature do not provide a consistent comparison. This paper addresses this gap by conducting a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes. Different state-of-the-art approaches are benchmarked on four text classification datasets, including a new dataset from the medical domain. Additionally, novel SimCSE [7] and SBERT-based [26] baselines are proposed, as other baselines used in existing work yield weak classification results and are easily outperformed. Finally, the novel similarity-based Lbl2TransformerVec approach is presented, which outperforms previous state-of-the-art approaches in unsupervised text classification. Our experiments show that similarity-based approaches significantly outperform zero-shot approaches in most cases. Additionally, using SimCSE or SBERT embeddings instead of simpler text representations increases similarity-based classification results even further.","PeriodicalId":315721,"journal":{"name":"Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130036007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval 2022年第六届自然语言处理与信息检索国际会议论文集

Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval Pub Date : 1900-01-01 DOI: 10.1145/3582768

引用次数: 0