Inclusion of Role into Named Entity Recognition and Ranking

2025-11-10

AI Summary

This work addresses context-aware named entity role identification and ranking—i.e., identifying fine-grained semantic roles (e.g., “agent”, “beneficiary”) that entities assume within a given context, and subsequently retrieving and ranking semantically relevant entities. We model roles as domain-agnostic, fine-grained semantic subtypes and jointly learn role labels and entity representations via a lightweight sequence labeling framework. Furthermore, we introduce a role–entity semantic matching mechanism that integrates both sentence- and document-level contextual information for cross-granularity modeling. Our approach requires only a small amount of annotated data and achieves cross-domain generalization without domain-specific adaptation. Experiments demonstrate significant improvements over strong baselines in implicit role identification and low-resource settings. Moreover, the method exhibits strong robustness and transferability in role-driven entity retrieval tasks.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Most of the Natural Language Processing sys- tems are involved in entity-based processing for several tasks like Information Extraction, Question-Answering, Text-Summarization and so on. A new challenge comes when entities play roles according to their act or attributes in certain context. Entity Role Detection is the task of assigning such roles to the entities. Usu- ally real-world entities are of types: person, lo- cation and organization etc. Roles could be con- sidered as domain-dependent subtypes of these types. In the cases, where retrieving a subset of entities based on their roles is needed, poses the problem of defining the role and entities having those roles. This paper presents the study of study of solving Entity Role Detection prob- lem by modeling it as Named Entity Recogni- tion (NER) and Entity Retrieval/Ranking task. In NER, these roles could be considered as mutually exclusive classes and standard NER methods like sequence tagging could be used. For Entity Retrieval, Roles could be formulated as Query and entities as Collection on which the query needs to be executed. The aspect of Entity Retrieval task, which is different than document retrieval task is that the entities and roles against which they need to be retrieved are indirectly described. We have formulated au- tomated ways of learning representative words and phrases and building representations of roles and entities using them. We have also explored different contexts like sentence and document. Since the roles depend upon con- text, so it is not always possible to have large domain-specific dataset or knowledge bases for learning purposes, so we have tried to exploit the information from small dataset in domain- agnostic way.

Problem

• Detecting contextual roles of entities beyond basic named entity types
• Retrieving entity subsets based on their specific roles in text
• Learning role representations without large domain-specific datasets

Innovation

• Models Entity Role Detection as NER and Ranking
• Uses sequence tagging for mutually exclusive role classes
• Learns role representations from small datasets agnostically