检索结果-内蒙古大学图书馆

Joint 30th International Conference on computational linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Troiano, Enrica Vossen, Piek Vrije Universiteit Amsterdam Computational Linguistics and Text Mining Lab Netherlands

ISBN: (纸本)9782493814104

We introduce CLAUSE-ATLAS, a resource of XIX and XX century English novels annotated automatically. This corpus, which contains 41,715 labeled clauses, allows to study stories as sequences of eventive, subjective and contextual information. We use it to investigate if recent large language models, in particular gpt-3.5-turbo with 16k tokens of context, constitute promising tools to annotate large amounts of data for literary studies (we show that this is the case). Moreover, by analyzing the annotations so collected, we find that our clause-based approach to literature captures structural patterns within books, as well as qualitative differences between them. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： ChatGPT events literary resources LLM-based annotation narrative theory subjectivity

来源：评论

学校读者我要写书评

暂无评论

Tracking Perspectives on Event Participants: a Structural Analysis of the Framing of Real-World Events in Co-Referential Corpora 1

Tracking Perspectives on Event Participants: a Structural An...

引用

1st Workshop on Reference, Framing, and Perspective, RFP 2024

作者： Remijnse, Levi Sommerauer, Pia Fokkens, Antske Vossen, Piek Vrije Universiteit Amsterdam Computational Linguistics and Text Mining Lab De Boelelaan 1105 Amsterdam1081 HV Netherlands

ISBN: (纸本)9782493814272

In this paper, we present the outcome of a structural linguistic analysis performed on a referentially grounded FrameNet dataset. In this dataset, multiple Dutch events are referenced by multiple co-referential Dutch news texts. Mentions in those documents are annotated with respect to their referential grounding (i.e., links to structured Wikidata), and their conceptual representation (i.e., frames). Provided with each document’s temporal reporting distance, we selected documents for two events - the Utrecht shooting and MH17 - and performed an analysis in which we tracked the events’ participants over time in both their focalization (number of mentions) and their framing (distribution of frame element labels). This way, we use the carefully collected and annotated data to schematize shifts in focalization and perspectivization of the participants as a result of the constantly developing narrative surrounding the events. This novel type of linguistic research involves reference to the real-world referents and takes into account storytelling in news streams. © 2024 ELRA Language Resource Association.

关键词： FrameNet narratology referential grounding

来源：评论

学校读者我要写书评

暂无评论

DEFVERIFY: Do Hate Speech Models Reflect Their Dataset's Definition? 31

DEFVERIFY: Do Hate Speech Models Reflect Their Dataset's Def...

引用

31st International Conference on computational linguistics, COLING 2025

作者： Khurana, Urja Nalisnick, Eric Fokkens, Antske Computational Linguistics and Text Mining Lab Vrije Universiteit Amsterdam Netherlands Department of Computer Science Johns Hopkins University United States

ISBN: (纸本)9798891761964

When building a predictive model, it is often difficult to ensure that application-specific requirements are encoded by the model that will eventually be deployed. Consider researchers working on hate speech detection. They will have an idea of what is considered hate speech, but building a model that reflects their view accurately requires preserving those ideals throughout the workflow of data set construction and model training. Complications such as sampling bias, annotation bias, and model misspecification almost always arise, possibly resulting in a gap between the application specification and the model's actual behavior upon deployment. To address this issue for hate speech detection, we propose DEFVERIFY: a 3-step procedure that (i) encodes a user-specified definition of hate speech, (ii) quantifies to what extent the model reflects the intended definition, and (iii) tries to identify the point of failure in the workflow. We use DEFVERIFY to find gaps between definition and model behavior when applied to six popular hate speech benchmark datasets. © 2025 Association for computational linguistics.

关键词： computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Topic-specific social science theory in stance detection: A proposal and interdisciplinary pilot study on sustainability initiatives 4

Topic-specific social science theory in stance detection: A ...

引用

4th Workshop on computational linguistics for the Political and Social Sciences, CPSS 2024

作者： Reuver, Myrthe Polimeno, Alessandra Fokkens, Antske Lopes, Ana Isabel Computational Linguistics & Text Mining Lab Vrije Universiteit Amsterdam Netherlands Communication Science Department Vrije Universiteit Amsterdam Netherlands Utrecht Data School Utrecht University Netherlands

ISBN: (纸本)9781952148255

Topic-specificity is often seen as a limitation of stance detection models and datasets, especially for analyzing political and societal debates. However, stances contain topic-specific aspects that are crucial for an in-depth understanding of these debates. Our interdisciplinary approach identifies social science theories on specific debate topics as an opportunity for further defining stance detection research and analyzing online debate. This paper explores sustainability as debate topic, and connects stance to the sustainability-related Value-Belief-Norm (VBN) theory. VBN theory states that arguments in favor or against sustainability initiatives contain the dimensions of feeling power to change the issue with the initiative, and thinking whether or not the initiative tackles an urgent threat to the environment. In a pilot study with our Reddit European Sustainability Initiatives corpus, we develop an annotation procedure for these complex concepts. We then compare crowd-workers with Natural Language Processing experts' annotation proficiency. Both crowd-workers and NLP experts find the tasks difficult, but experts reach more agreement on some difficult examples. This pilot study shows that complex theories about debate topics are feasible and worthwhile as annotation tasks for stance detection. ©2020 Association for computational linguistics.

关键词： Sustainable development goals

来源：评论

学校读者我要写书评

暂无评论

Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language Models

Methodological Insights in Detecting Subtle Semantic Shifts ...

引用

2023 Findings of the Association for computational linguistics: EMNLP 2023

作者： Hoeken, Sanne Alaçam, Özge Fokkens, Antske Sommerauer, Pia Computational Linguistics Dept. of Linguistics Bielefeld University Germany Center for Information and Language Processing LMU Munich Germany Computational Linguistics & Text Mining Lab Vrije Universiteit Amsterdam Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9798891760615

In this paper, we investigate automatic detection of subtle semantic shifts between social communities of different political convictions in Dutch and English. We perform a methodological study comparing methods using static and contextualized language models. We investigate the impact of specializing contextualized models through fine-tuning on target corpora, word sense disambiguation and sentiment. We furthermore propose a new approach using masked token prediction which relies on behavioral information, specifically the most probable substitutions, instead of geometrical comparison of representations. Our results show that methods using static models and our masked token prediction method can detect differences in connotation of politically loaded terms, whereas methods that rely on measuring the distance between contextualized representations are not providing clear signals, even in synthetic scenarios of extreme shifts. © 2023 Association for computational linguistics.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Extracting triples from dialogues for conversational social agents

arXiv

引用

arXiv 2024年

作者： Vossen, Piek Santamaría, Selene Báez Bajčetić, Lenka Belluci, Thomas Computational Linguistics and Text Mining Lab Vrije Universiteit Amsterdam Amsterdam Netherlands

Obtaining an explicit understanding of communication within a Hybrid Intelligence collaboration is essential to create controllable and transparent agents. In this paper, we describe a number of Natural Language Understanding models that extract explicit symbolic triples from social conversation. Triple extraction has mostly been developed and tested for Knowledge Base Completion using Wikipedia text and data for training and testing. However, social conversation is very different as a genre in which interlocutors exchange information in sequences of utterances that involve statements, questions, and answers. Phenomena such as co-reference, ellipsis, coordination, and implicit and explicit negation or confirmation are more prominent in conversation than in Wikipedia text. We therefore describe an attempt to fill this gap by releasing data sets for training and testing triple extraction from social conversation. We also created five triple extraction models and tested them in our evaluation data. The highest precision is 51.14 for complete triples and 69.32 for triple elements when tested on single utterances. However, scores for conversational triples that span multiple turns are much lower, showing that extracting knowledge from true conversational data is much more challenging. © 2024, CC BY.

关键词： Chatbots

来源：评论

学校读者我要写书评

暂无评论

Improving and Evaluating the Detection of Fragmentation in News Recommendations with the Clustering of News Story Chains 1

Improving and Evaluating the Detection of Fragmentation in N...

引用

1st Workshop on the Normative Design and Evaluation of Recommender Systems, NORMalize 2023

作者： Polimeno, Alessandra Reuver, Myrthe Vrijenhoek, Sanne Fokkens, Antske Utrecht Data School Utrecht University Netherlands Computational Linguistics & Text Mining Lab Vrije Universiteit Amsterdam Netherlands Institute for Information Law University of Amsterdam Netherlands

News recommender systems play an increasingly influential role in shaping information access within democratic societies. However, tailoring recommendations to users' specific interests can result in the divergence of information streams. Fragmented access to information poses challenges to the integrity of the public sphere, thereby influencing democracy and public discourse. The Fragmentation metric quantifies the degree of fragmentation of information streams in news recommendations. Accurate measurement of this metric requires the application of Natural Language Processing (NLP) to identify distinct news events, stories, or timelines. This paper presents an extensive investigation of various approaches for quantifying Fragmentation in news recommendations. These approaches are evaluated both intrinsically, by measuring performance on news story clustering, and extrinsically, by assessing the Fragmentation scores of different simulated news recommender scenarios. Our findings demonstrate that agglomerative hierarchical clustering coupled with SentenceBERT text representation is substantially better at detecting Fragmentation than earlier implementations. Additionally, the analysis of simulated scenarios yields valuable insights and recommendations for stakeholders concerning the measurement and interpretation of Fragmentation. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study 30

Investigating the Robustness of Modelling Decisions for Few-...

引用

Joint 30th International Conference on computational linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Reuver, Myrthe Verberne, Suzan Fokkens, Antske Computational Linguistics and Text Mining Lab Vrije Universiteit Amsterdam Netherlands Leiden Institute of Advanced Computer Science Leiden University Netherlands Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9782493814104

For a viewpoint-diverse news recommender, identifying whether two news articles express the same viewpoint is essential. One way to determine"same or different" viewpoint is stance detection. In this paper, we investigate the robustness of operationalization choices for few-shot stance detection, with special attention to modelling stance across different topics. Our experiments test pre-registered hypotheses on stance detection. Specifically, we compare two stance task definitions (Pro/Con versus Same Side Stance), two LLM architectures (bi-encoding versus cross-encoding), and adding Natural Language Inference knowledge, with pre-trained RoBERTa models trained with shots of 100 examples from 7 different stance detection datasets. Some of our hypotheses and claims from earlier work can be confirmed, while others give more inconsistent results. The effect of the Same Side Stance definition on performance differs per dataset and is influenced by other modelling choices. We found no relationship between the number of training topics in the training shots and performance. In general, cross-encoding out-performs bi-encoding, and adding NLI training to our models gives considerable improvement, but these results are not consistent across all datasets. Our results indicate that it is essential to include multiple datasets and systematic modelling experiments when aiming to find robust modelling choices for the concept 'stance'. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Encoding (symbols)

来源：评论

学校读者我要写书评

暂无评论

Towards quantifying politicization in foreign aid project reports 2

Towards quantifying politicization in foreign aid project re...

引用

2nd Workshop on Natural Language Processing for Political Sciences, PoliticalNLP 2024

作者： Wang, Sidi Eggers, Gustav de Roode Torres Georgiadis, Alexia Đo, Tuan Anh Gontard, Léa Carlitz, Ruth Bloem, Jelke Data Science Centre University of Amsterdam Netherlands Computational Linguistics and Text Mining Lab Vrije Universiteit Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Netherlands Department of Political Science University of Amsterdam Netherlands

ISBN: (纸本)9782493814265

We aim to develop a metric of politicization by investigating whether this concept can be operationalized computationally using document embeddings. We are interested in measuring the extent to which foreign aid is politicized. textual reports of foreign aid projects are often made available by donor governments, but these are large and unstructured. By embedding them in vector space, we can compute similarities between sets of known politicized keywords and the foreign aid reports. We present a pilot study where we apply this metric to USAID reports. © 2024 ELRA Language Resource Association.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projection

Better Hit the Nail on the Head than Beat around the Bush: R...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Haghighatkhah, Pantea Fokkens, Antske Sommerauer, Pia Speckmann, Bettina Verbeek, Kevin TU Eindhoven Department of Mathematics and Computer Science Netherlands Vrije Universiteit Amsterdam Computational Linguistics Text Mining Lab Netherlands

Bias elimination and recent probing studies attempt to remove specific information from embedding spaces. Here it is important to remove as much of the target information as possible, while preserving any other information present. INLP is a popular recent method which removes specific information through iterative nullspace projections. Multiple iterations, however, increase the risk that information other than the target is negatively affected. We introduce two methods that find a single targeted projection: Mean Projection (MP, more efficient) and Tukey Median Projection (TMP, with theoretical guarantees). Our comparison between MP and INLP shows that (1) one MP projection removes linear separability based on the target and (2) MP has less impact on the overall space. Further analysis shows that applying random projections after MP leads to the same overall effects on the embedding space as the multiple projections of INLP. Applying one targeted (MP) projection hence is methodologically cleaner than applying multiple (INLP) projections that introduce random effects. © 2022 Association for computational linguistics.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：