Papers with NLP methods
NLP for Conversations: Sentiment, Summarization, and Group Dynamics (C18-3)
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| Challenge: | a tutorial focuses on computational models for conversational structure, summarization and sentiment detection, and group dynamics. |
| Approach: | a tutorial will provide examples of specific NLP tasks for conversational structure, summarization and sentiment detection, and group dynamics. |
| Outcome: | The tutorial focuses on the three areas of conversational structure, summarization and sentiment detection, and group dynamics. |
Eye Tracking and NLP (2025.acl-tutorials)
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| Challenge: | tutorial combines eye tracking during reading with NLP . outlines how eye movements in reading can be leveraged for NLP methods . |
| Approach: | The tutorial combines eye tracking during reading with NLP . it covers eye movements in reading, integrating eye movement data in NLP models . |
| Outcome: | The tutorial outlines how eye movements in reading can be leveraged for NLP . it provides the essential background for conducting research on joint modeling of eye movements and text. |
Regularized Graph Convolutional Networks for Short Text Classification (2020.coling-industry)
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| Challenge: | Short text classification is a problem in natural language processing, social network analysis, and e-commerce. |
| Approach: | They propose a short text classification technique that incorporates label dependencies into the output space to overcome the limitations of short text. |
| Outcome: | The proposed model outperforms baseline methods on proprietary and external datasets and is more robust to noise in textual features. |
GrapAL: Connecting the Dots in Scientific Literature (P19-3)
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| Challenge: | Several software tools are available to help researchers perform tasks such as searching for papers, assessing applicants for a research position and keeping track of papers published on topics of interest. |
| Approach: | They introduce a graph database of academic literature with an intuitive schema and query language . they open source the demo code to help other researchers develop applications that build on it . |
| Outcome: | The proposed tool can be used to find experts on a given topic for peer review, find indirect connections between biomedical entities, and compute citation-based metrics. |
The Why and The How: A Survey on Natural Language Interaction in Visualization (2022.naacl-main)
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| Challenge: | Recent research shows that different forms of natural language-based interaction prove suitable to support users in accomplishing various visualization tasks. |
| Approach: | They propose a taxonomy of visualization tasks and a classification system to illustrate the state-of-the-art of natural language-based interaction in visualization. |
| Outcome: | The proposed model can support annotations, recommendations, explanations, and documentation tasks. |
Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation (2022.acl-long)
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| Challenge: | Recent studies have found that the performance of multilingual pretrained models is highly dependent on the availability of monolingual or parallel text in a target language. |
| Approach: | They propose to use bilingual lexicons to synthesize textual or labeled data and combine it with monolingual or parallel text when available. |
| Outcome: | The proposed methods improve performance for 19 under-represented languages with and without extra monolingual text. |
Meaning Variation and Data Quality in the Corpus of Founding Era American English (2025.acl-short)
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| Challenge: | Legal scholars are increasingly using corpus based methods for assessing historical meaning . main corpus used in legal arguments is the Corpus of Founding Era American English . |
| Approach: | They demonstrate how NLP can be used to infer meaning change and variation using masked language models. |
| Outcome: | The proposed method can be used to infer meaning change and variation using advanced methods. |
Detecting Denial-of-Service Attacks from Social Media Text: Applying NLP to Computer Security (N18-1)
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| Challenge: | Distributed Denial of Service (DDoS) attacks are becoming more frequent and more severe in their impact. |
| Approach: | They propose a feed-forward neural network and a partially labeled LDA model that use social media as an indirect measure of network service status. |
| Outcome: | The proposed model outperforms previous work by significant margins and provides the first fine-grained analysis of how the public reacts to ongoing network attacks. |
How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models (2025.findings-naacl)
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| Challenge: | Cantonese has scant representation in NLP research, especially compared to other languages from similarly developed regions. |
| Approach: | They propose to evaluate Cantonese LLM performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantonesian. |
| Outcome: | The proposed models will evaluate Cantonese's performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantone. |
Multi-Aspect Transfer Learning for Detecting Low Resource Mental Disorders on Social Media (2022.lrec-1)
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| Challenge: | Mental disorders are an important and pervasive public health issue. |
| Approach: | They propose to use linguistic features to improve mental disorder detection . they propose to apply multi-aspect transfer learning to detecting disorders from social media . |
| Outcome: | The proposed methods can be used to improve mental disorder detection in the context of data scarcity and understanding the overlapping symptoms between disorders. |
Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness (2024.findings-acl)
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| Challenge: | Existing studies have studied dialect-related fairness for aspects like hate speech, but other aspects of biased language remain unexplored. |
| Approach: | They propose a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. |
| Outcome: | The proposed approach improves dialect learning and detects biases more reliably. |
Multilingual Event Extraction from Historical Newspaper Adverts (2023.acl-long)
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| Challenge: | Developing NLP methods for historical corpora is difficult, as only domain experts can label them . off-the-shelf models are trained on modern language texts, rendering them weaker for historical documents . |
| Approach: | They propose to use an annotated newspaper dataset to extract historical data from a novel domain of texts. |
| Outcome: | The proposed method performs well on a multilingual dataset in English, French, and Dutch . it is possible to extract surprisingly good results even with scarce annotated data using existing models and datasets for modern languages . |
Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers (2023.emnlp-main)
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| Challenge: | Scaling analysis is a technique that assigns a political actor a score on a predefined scale based on 'typically long' text. |
| Approach: | They propose to use label aggregation and long-input-Transformer-based models to automatically scale political-party manifestos. |
| Outcome: | The proposed models can scale political platforms on a predefined scale based on 'left-right' scales and work robustly across domains and languages. |
German Parliamentary Corpus (GerParCor) Reloaded (2024.lrec-main)
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| Challenge: | In 2022, the largest German-speaking corpus of parliamentary protocols from three different centuries has been published - GerParCor. |
| Approach: | They propose to update the largest German-speaking corpus of parliamentary protocols from three different centuries, on a national and federal level, from Germany, Austria, Switzerland and Liechtenstein, and to make them available in XMI format. |
| Outcome: | The updated corpus includes all new parliamentary protocols and adds and preprocesses further parliamentary protocol not covered in the previous version. |
-Stance: A Large-Scale Real World Dataset of Stances in Legal Argumentation (2025.acl-long)
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| Challenge: | Current tools for legal argument reasoning do not support this task. |
| Approach: | They propose to use a large-scale dataset to facilitate work on the legal argument stance classification task by evaluating whether a case summary strengthens or weakens a legal argument. |
| Outcome: | The proposed dataset is used to facilitate work on the legal argument stance classification task, which involves assessing whether a case summary strengthens or weakens a legal argument (polarity) and to what extent (intensity). |