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检索条件"任意字段=7th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2012"

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Advances in neural text generation: A systematic review (2022-2024) 7

Advances in neural text generation: A systematic review (202...

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7th workshop for Young Scientists in Computer Science and Software Engineering, CS and SE@SW 2024

作者： Slobodianiuk, Artem V. Semerikov, Serhiy O. Kryvyi Rih State Pedagogical University 54 Universytetskyi Ave. Kryvyi Rih50086 Ukraine Institute for Digitalisation of Education The NAES of Ukraine 9 M. Berlynskoho Str. Kyiv04060 Ukraine Zhytomyr Polytechnic State University 103 Chudnivsyka Str. Zhytomyr10005 Ukraine Kryvyi Rih National University 11 Vitalii Matusevych Str. Kryvyi Rih50027 Ukraine Academy of Cognitive and Natural Sciences 54 Universytetskyi Ave. Kryvyi Rih50086 Ukraine

Recent years have witnessed significant advancements in neural text generation driven by the emergence of large language models and growing interest in this field. this systematic review aims to identify and summarize current trends, approaches, and methods in neural text generation from 2022 to 2024, complementing the findings of a previous review covering 2015-2021. Following the PRISMA methodology, 43 articles were selected from the Scopus database for analysis. the review reveals a shift towards innovative model architectures like Transformer-based models (GPT-2, GPT-3, BERT), attention mechanisms, and controllable text generation. While BLEU, ROUGE, and human evaluation remain the most popular evaluation metrics, new metrics like BERTScore have emerged. Datasets span diverse domains and data types, with growing interest in unlabeled data. Applications have expanded to areas such as table-to-text generation, knowledge graph-based generation, and medical text generation. Although English dominates, there is increasing research on low-resource languages. the findings highlight the rapid evolution of neural text generation methods, the broadening of application areas, and promising avenues for future research. © 2025 Copyright for this paper by its authors.

关键词： applications datasets deep learning evaluation metrics low-resource languages natural language processing neural text generation systematic review

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Topic-based chinese message polarity classification system at SIGHAN8-Task2 8

Topic-based chinese message polarity classification system a...

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8th SIGHAN workshop on Chinese language processing, SIGHAN 2015

作者： Liao, Chun Feng, Chong Yang, Sen Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology China

ISBN: (纸本)9781941643570

this paper describes the topic-based Chinese message polarity classification system submitted by LCYS-TEAM at SIGHAN8-Task2. the system mainly includes two parts: 1) a graph-based ranking model integrating local and global information is adopted to represent the classification ability of words towards different topics. In construction of graph model, a new weighting approach and a PMI-based random jumping probability selection method is proposed. 2) For sentimental features, word embedding is employed for acquiring expanded topical words and syntactic dependency is adopted for getting topic-related sentimental words. Experiment results demonstrate the effectiveness of our system. © 2015 Proceedings of the 8th SIGHAN workshop on Chinese language processing, SIGHAN 2015 - co-located with 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on natural language processing, ACL IJCNLP 2015. All rights reserved.

关键词： graphic methods

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Chinese named entity recognition with graph-based semi-supervised learning model 8

Chinese named entity recognition with graph-based semi-super...

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8th SIGHAN workshop on Chinese language processing, SIGHAN 2015

作者： Han, Aaron Li-Feng Zeng, Xiaodong Wong, Derek F. Chao, Lidia S. Institute for Logic Language and Computation University of Amsterdam Science Park 107 Amsterdam1098 Netherlands NLP2CT Laboratory Department of Computer and Information Science University of Macau Macau China

ISBN: (纸本)9781941643570

Named entity recognition (NER) plays an important role in the NLP literature. the traditional methods tend to employ large annotated corpus to achieve a high performance. Different with many semi-supervised learning models for NER task, in this paper, we employ the graph-based semi-supervised learning (GBSSL) method to utilize the freely available unlabeled data. the experiment shows that the unlabeled corpus can enhance the state-of-theart conditional random field (CRF) learning model and has potential to improve the tagging accuracy even though the margin is a little weak and not satisfying in current experiments. © 2015 Proceedings of the 8th SIGHAN workshop on Chinese language processing, SIGHAN 2015 - co-located with 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on natural language processing, ACL IJCNLP 2015. All rights reserved.

关键词： natural language processing systems

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Sar-graphs: A Linked Linguistic Knowledge Resource Connecting Facts with language 4

Sar-graphs: A Linked Linguistic Knowledge Resource Connectin...

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4th workshop on Linked Data in Linguistics: Resources and Applications, LDL 2015

作者： Krause, Sebastian Hennig, Leonhard Gabryszak, Aleksandra Xu, Feiyu Uszkoreit, Hans DFKI Language Technology Lab Berlin Germany

ISBN: (纸本)9781941643570

We present sar-graphs, a knowledge resource that links semantic relations from factual knowledge graphs to the linguistic patterns with which a language can express instances of these relations. Sar-graphs expand upon existing lexicosemantic resources by modeling syntactic and semantic information at the level of relations, and are hence useful for tasks such as knowledge base population and relation extraction. We present a languageindependent method to automatically construct sar-graph instances that is based on distantly supervised relation extraction. We link sar-graphs at the lexical level to BabelNet, WordNet and UBY, and present our ongoing work on pattern- and relationlevel linking to FrameNet. An initial dataset of English sar-graphs for 25 relations is made publicly available, together with a Java-based API. © 2015 Association for Computational Linguistics and Asian Federation of natural language processing.

关键词： graphic methods

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Exploiting timegraphs in temporal relation classification 9

Exploiting timegraphs in temporal relation classification

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9th workshop on graph-based methods for natural language processing, textgraphs 2014, in conjunction with the Conference on Empirical methods in natural language processing, EMNLP 2014

作者： Laokulrat, Natsuda Miwa, Makoto Tsuruoka, Yoshimasa University of Tokyo 3-7-1 Hongo Bunkyo-ku Tokyo Japan Toyota Technological Institute 2-12-1 Hisakata Tempaku-ku Nagoya Japan

ISBN: (纸本)9781937284961

Most of the recent work on machine learning-based temporal relation classification has been done by considering only a given pair of temporal entities (events or temporal expressions) at a time. Entities that have temporal connections to the pair of temporal entities under inspection are not considered even though they provide valuable clues to the prediction. In this paper, we present a new approach for exploiting knowledge obtained from nearby entities by making use of timegraphs and applying the stacked learning method to the temporal relation classification task. By performing 10-fold cross validation on the Timebank corpus, we achieved an F1 score of 59.61% based on the graph-based evaluation, which is 0.16 percentage points higher than that of the local approach. Our system outperformed the state-of-the-art system that utilizes global information and achieved about 1.4 percentage points higher accuracy. © 2014 Association for Computational Linguistics

关键词： graphic methods

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Understanding seed selection in bootstrapping 8

Understanding seed selection in bootstrapping

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8th workshop on graph-based methods for natural language processing, textgraphs 2013, at the Conference on Empirical methods in natural language processing, EMNLP 2013

作者： Ehara, Yo Sato, Issei Oiwa, Hidekazu Nakagawa, Hiroshi Graduate School of Information Science and Technology United States Information Technology Center University of Tokyo / 7-3-1 Hongo Bunkyo-ku Tokyo Japan JSPS Research Fellow Kojimachi Business Center Building 5-3-1 Kojimachi Chiyoda-ku Tokyo Japan

ISBN: (纸本)9781937284978

Bootstrapping has recently become the focus of much attention in natural language processing to reduce labeling cost. In bootstrapping, unlabeled instances can be harvested from the initial labeled "seed" set. the selected seed set affects accuracy, but how to select a good seed set is not yet clear. thus, an "iterative seeding" framework is proposed for bootstrapping to reduce its labeling cost. Our framework iteratively selects the unlabeled instance that has the best "goodness of seed" and labels the unlabeled instance in the seed set. Our framework deepens understanding of this seeding process in bootstrapping by deriving the dual problem. We propose a method called expected model rotation (EMR) that works well on not well-separated data which frequently occur as realistic data. Experimental results show that EMR can select seed sets that provide significantly higher mean reciprocal rank on realistic data than existing naive selection methods or random seed sets. © 2013 Association for Computational Linguistics

关键词： Iterative methods

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Dense semantic graph and its application in single document summarisation 7

Dense semantic graph and its application in single document ...

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7th International workshop on Information Filtering and Retrieval, DART 2013 - workshop of the 13th AI*IA Conference

作者： Joshi, Monika Wang, Hui McClean, Sally University of Ulster Co. AntrimBT37 0QB United Kingdom University of Ulster Co. LondonderryBT52 1SA United Kingdom

Semantic graph representation of text is an important part of natural language processing applications such as text summarisation. We have studied two ways of constructing the semantic graph of a document from dependency parsing of its sentences. the first graph is derived from the subject-object-verb representation of sentence, and the second graph is derived from considering more dependency relations in the sentence by a shortest distance dependency path calculation, resulting in a dense semantic graph. We have shown through experiments that dense semantic graphs gives better performance in semantic graph based unsupervised extractive text summarisation. Copyright © 2013 for the individual papers by the papers' authors.

关键词： graphic methods

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Summarizing conceptual graphs for automatic summarization task

Summarizing conceptual graphs for automatic summarization ta...

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7th International Symposium on Parameterized and Exact Computation, IPEC 2012

作者： Miranda-Jiménez, Sabino Gelbukh, Alexander Sidorov, Grigori Natural Language and Text Processing Laboratory Center for Computing Research National Polytechnic Institute Av. J. de D. Batiz s/n esq. M. Col. N. Indust. Vallejo 07738 Mexico City Mexico

ISBN: (纸本)9783642332920

We propose a conceptual graph-based framework for abstractive text summarization. While syntactic or partial semantic representations of texts have been used in literature, complete semantic representations have not been explored for this purpose. We use a complete semantic representation, namely, conceptual graph structures, composed of concepts and conceptual relations. To summarize a conceptual graph, we remove the nodes that represent less important content, and apply certain operations on the resulting smaller conceptual graphs. We measure the importance of nodes on weighted conceptual graphs by the HITS algorithm, augmented with some heuristics based on VerbNet semantic patterns. Our experimental results are promising. © 2013 Springer-Verlag.

关键词： graphic methods

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7th workshop on graph-based methods for natural language processing, textgraphs 2012

作者： Minkov, Einat Cohen, William W. Dep. of Information Systems University of Haifa Haifa 31905 Israel School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9781937284374

We learn graph-based similarity measures for the task of extracting word synonyms from a corpus of parsed text. A constrained graph walk variant that has been successfully applied in the past in similar settings is shown to outperform a state-of-the-art syntactic vectorbased approach on this task. Further, we show that learning specialized similarity measures for different word types is advantageous. © 2012 the Association for Computational Linguistics.

关键词： graphic methods

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ACL 2012 - textgraphs 2012: workshop on graph-based methods for natural language processing, workshop Proceedings

ACL 2012 - TextGraphs 2012: Workshop on Graph-Based Methods ...

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7th workshop on graph-based methods for natural language processing, textgraphs 2012

ISBN: (纸本)9781937284374

the proceedings contain 8 papers. the topics discussed include: a new parametric estimation method for graph-based clustering;extracting signed social networks from text;using link analysis to discover interesting messages spread across twitter;graph based similarity measures for synonym extraction from parsed text;semantic relatedness for biomedical word sense disambiguation;identifying untyped relation mentions in a corpus given an ontology;cause-effect relation learning;and bringing the associative ability to social tag recommendation.

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