Papers with analytics
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop) (2025.naacl-srw)
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| Challenge: | Philip is a member of the Association for Computational Linguistics and is pursuing his PhD in computational linguistics. |
| Approach: | Philip is a member of the Association for Computational Linguistics and is pursuing a PhD in computational linguistics. |
| Outcome: | Philip is a member of the Association for Computational Linguistics and is pursuing two PhDs in computational linguistics and cognitive neuroscience. |
RelationalCoder: Rethinking Complex Tables via Programmatic Relational Transformation (2025.acl-long)
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| Challenge: | Semi-structured tables remain a major obstacle for automated data processing and analytics. |
| Approach: | They propose a technique called Loop Reference Decoding which identifies expandable groups and replicates each group using a concise loop over its repetitive region. |
| Outcome: | The proposed technique reduces output length from O(N M) to approximately O(K) Extensive experiments on HiTab and MultiHiertt show that it boosts Llama-2 and Mistral models by more than 20%, and GPT-4o by over 4%. |
Group, Embed and Reason: A Hybrid LLM and Embedding Framework for Semantic Attribute Alignment (2025.emnlp-industry)
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Shramona Chakraborty, Shashank Mujumdar, Nitin Gupta, Sameep Mehta, Ronen Kat, Itay Etelis, Mohamed Mahameed, Itai Guez, Rachel Tzoref-Brill
| Challenge: | a framework to align attributes that refer to the same concept but differ across schemas is challenging in schema only settings where no instance data is available due to ambiguous names, inconsistent descriptions, and domain-specific terminologies. |
| Approach: | They propose a framework that combines contextual reasoning and embedding-based similarity to address token limitations and hallucinations. |
| Outcome: | The proposed framework scales to large schemas and shows strong performance on healthcare schemas. |
S2abEL: A Dataset for Entity Linking from Scientific Tables (2023.emnlp-main)
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| Challenge: | Entity linking (EL) is a longstanding problem in natural language processing and information extraction. |
| Approach: | They propose a neural baseline method for EL on scientific tables containing many out-of-knowledge-base mentions and a method that significantly outperforms a generic table EL method. |
| Outcome: | The proposed method significantly outperforms state-of-the-art generic table EL method on scientific tables with many out-of knowledge-base mentions. |