Papers with entity identification
Process-Level Representation of Scientific Protocols with Interactive Annotation (2021.eacl-main)
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| Challenge: | Existing efforts to automate wet lab workflows are focusing on graph-prediction models that capture both concrete, exact quantities ("30 minutes") and vague instructions ("swirl") |
| Approach: | They manually annotate PEGs in a corpus of complex lab protocols with a novel interactive textual simulator that keeps track of entity traits and semantic constraints during annotation. |
| Outcome: | The proposed graph-prediction models are good at entity identification and local relation extraction while addressing challenges such as cross-sentence relations and long-range coreference. |
Regular Expression Guided Entity Mention Mining from Noisy Web Data (D18-1)
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| Challenge: | Named Entity Recognition (NER) is a subtask of the broader problem of Information Extraction (IE) from text. |
| Approach: | They propose a framework that uses Regular Expressions to identify entities from web data . they combine expressive power of REs with ability of deep learning to learn from large data a human expert is asked to label a small set of documents . |
| Outcome: | The proposed framework achieves impressive accuracy while requiring modest human effort. |
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks (2023.emnlp-main)
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| Challenge: | Entity and Relation Extraction (ERE) is an important task in information extraction. |
| Approach: | They propose a hypergraph neural network for ERE built upon the PL-marker . they use a pruner mechanism to transfer the burden of entity identification to the joint module . |
| Outcome: | The proposed model improves on three widely used benchmarks on ERE task . it uses a pruner mechanism to transfer the burden of entity identification to the joint module . |
Ground Then Rank: Revisiting Knowledge-Based VQA with Training-Free Entity Identification (2026.findings-acl)
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| Challenge: | Existing multi-modal retrieval augmented generation (MM-RAG) methods tightly couple entity discrimination and section-level evidence ranking into a single re-ranking stage, leading to high cost and limited generalization. |
| Approach: | They propose a framework that decouples entity identification from section-level re-ranking. |
| Outcome: | The proposed framework outperforms fine-tuned multi-modal re-ranking baselines while reducing training and inference complexity. |
STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing models focus on predictive accuracy over reasoning, a gap exists . time series data are ubiquitous in real-world systems and exhibit complex spatio-temporal structures. |
| Approach: | They propose a time series reasoning model that integrates time series, graph structure, and text for explicit reasoning. |
| Outcome: | The proposed model achieves average accuracy gains between 17% and 135% at 0.004x the cost of proprietary models and generalizes robustly to real-world data. |
Infrastructure for Semantic Annotation in the Genomics Domain (2020.lrec-1)
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Mahmoud El-Haj, Nathan Rutherford, Matthew Coole, Ignatius Ezeani, Sheryl Prentice, Nancy Ide, Jo Knight, Scott Piao, John Mariani, Paul Rayson, Keith Suderman
| Challenge: | a novel infrastructure for biomedical text mining combines NLP and corpus linguistics methods to provide a comprehensive corpus for literature-based discovery. |
| Approach: | They propose a novel pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature . it uses an updatable Gene Ontology Semantic Tagger and a NLP pipeline scheduler to collect and process the corpus. |
| Outcome: | The proposed infrastructure allows for extreme-scale research on the open access PubMed Central archive. |