Papers with mining
Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization (P19-2)
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| Challenge: | Existing research focuses on mining for opinions from review texts and ignores reviewers. |
| Approach: | They propose to model reviewer biases from review texts and learn a bias-aware opinion representation. |
| Outcome: | The proposed method includes balanced opinions from reviewers with different biases and preferences. |
Knowledge Discovery and Hypothesis Generation from Online Patient Forums: A Research Proposal (P19-2)
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| Challenge: | Unprompted patient experiences on patient forums contain a wealth of unexploited knowledge. |
| Approach: | They propose to develop automated methods for mining, aggregating and cross-linking patient knowledge from online forums. |
| Outcome: | The proposed methods could be compared with biomedical literature and provide hypotheses for future clinical research. |
BPID: A Benchmark for Personal Identity Deduplication (2024.emnlp-industry)
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Runhui Wang, Yefan Tao, Adit Krishnan, Luyang Kong, Xuanqing Liu, Yuqian Deng, Yunzhao Yang, Henrik Johnson, Andrew Borthwick, Shobhit Gupta, Aditi Gundlapalli, Davor Golac
| Challenge: | Data deduplication is a critical task in data management and mining, focused on consolidating duplicate records that refer to the same entity. |
| Approach: | They propose to use a dataset with 1,000,000 unlabeled synthetic PII profiles and a subset of 10,000 pairs curated and labeled as matches or non-matches. |
| Outcome: | The proposed datasets contain synthetic profiles built from publicly available sources that do not represent real individuals. |
Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation (2022.tacl-1)
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| Challenge: | Existing research on argumentation models does not provide a systematic overview of the types of knowledge required in CA tasks. |
| Approach: | They propose a taxonomy of the types of knowledge required in CA tasks . authors propose exploitation of these knowledge types for four main research areas . |
| Outcome: | The proposed taxonomy proposes a systematic overview of the types of knowledge required in CA tasks. |
Neural Mixed Counting Models for Dispersed Topic Discovery (2020.acl-main)
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| Challenge: | Existing methods for inference of parameter parameters are time-consuming and difficult to use. |
| Approach: | They propose two efficient neural mixed counting models that use the negative binomial distribution as the prior for dispersed topic discovery. |
| Outcome: | The proposed models outperform state-of-the-art models in terms of perplexity and topic coherence on real-world datasets. |
MARIO-0.5B: A Multi-Agent Lightweight Model for Real-Time Open Information Extraction in Low-Resource Settings (2025.findings-emnlp)
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| Challenge: | Large language models have shown remarkable capabilities in open information extraction, but their resource requirements often restrict their deployment in resource-constrained industrial settings. |
| Approach: | They introduce an ultra-lightweight large language model trained on instruction-based samples in Chinese, English, Korean, and Russian. |
| Outcome: | The proposed model outperforms large-scale models with up to 70B parameters, reducing computational resources by 140x and delivering 11x faster response times. |
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse (2025.findings-acl)
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| Challenge: | Visual Language Action models have shown promise in decision-making tasks, but have been neglected in previous work . |
| Approach: | They propose a new paradigm for visual language action models that enhances the foundation model prior to action-specific tuning by first post-training it on a curated set of visual and linguistic tasks using self-supervised learning. |
| Outcome: | The proposed model outperforms the best agent baseline on a diverse set of atomic tasks and surpasses imitation learning-based policies in Minecraft. |