What is Coreference Resolution?
Coreference Resolution is an NLP technique that identifies when different words or phrases in a text refer to the same real-world entity, such as recognizing that "the company," "it," and "Grab" all refer to the same organization within a document.
What Is Coreference Resolution?
Coreference Resolution is a Natural Language Processing task that determines when two or more expressions in text refer to the same entity. In the sentences "Grab expanded into new markets last quarter. The company reported strong growth. It plans to continue expansion in 2026," coreference resolution identifies that "Grab," "The company," and "It" all refer to the same entity.
This might seem simple for humans, who resolve coreferences effortlessly during reading. But for machines, tracking which pronouns and noun phrases refer to which entities is one of the most challenging problems in NLP. Getting it wrong leads to fundamental misunderstandings — confusing who said what, who did what, and what happened to whom.
For business leaders, coreference resolution is a critical capability that determines the accuracy of document analysis, customer interaction understanding, and information extraction. When your AI system reads a contract and needs to understand which "party" is responsible for which obligation, or when your chatbot needs to track what "it" refers to across a multi-turn conversation, coreference resolution is doing the heavy lifting.
How Coreference Resolution Works
Types of Coreference
Pronominal Coreference: Connecting pronouns to their antecedents. "Sarah joined the meeting. She presented the quarterly results." Here, "She" refers to "Sarah."
Nominal Coreference: Connecting different noun phrases. "The startup raised $10M in Series A. The young company plans to expand regionally." "The young company" refers to "The startup."
Event Coreference: Identifying when different descriptions refer to the same event. "The merger was announced Friday. The deal brings together two industry leaders." "The merger" and "The deal" refer to the same event.
Approaches to Coreference Resolution
Rule-Based Systems Early systems used linguistic rules: pronouns typically refer to the most recent matching noun, subjects are preferred over objects, and grammatical constraints (gender, number) filter candidates. These systems are fast but struggle with complex texts.
Statistical Models These systems learn patterns from annotated data, using features like distance between mentions, syntactic position, semantic similarity, and discourse structure to predict coreference relationships.
Neural Models Modern coreference systems use deep learning to create rich representations of each mention and its context, then score potential coreference links. End-to-end neural models achieve the highest accuracy by jointly learning mention detection and coreference linking.
Large Language Model Approaches The newest approaches leverage large language models that can resolve coreferences based on deep contextual understanding, handling even complex cases involving world knowledge and pragmatic reasoning.
The Resolution Process
- Mention detection — Identify all noun phrases and pronouns that could refer to entities
- Feature extraction — Gather information about each mention including position, grammatical role, semantic type, and surrounding context
- Candidate generation — For each mention, identify potential antecedent mentions
- Scoring — Rate the likelihood that each pair of mentions refers to the same entity
- Clustering — Group all mentions referring to the same entity into coreference chains
Business Applications of Coreference Resolution
Document Summarization Accurate summarization requires tracking entities throughout a document. Without coreference resolution, summaries may contain ambiguous pronouns or fail to connect related statements about the same entity.
Information Extraction at Scale When extracting facts from large document collections, coreference resolution ensures that all information about an entity is aggregated correctly. If a news article mentions "Google" in paragraph one and "the tech giant" in paragraph five, coreference resolution connects both mentions to the same entity.
Customer Conversation Understanding In multi-turn customer support conversations, customers frequently use pronouns and indirect references. "I bought the laptop last week. It stopped working yesterday. Can you replace it?" Coreference resolution helps support systems understand that "it" refers to "the laptop" across the entire conversation.
Legal Document Analysis Contracts often use complex reference chains: "The Licensor grants the Licensee... The former shall provide... The latter agrees to..." Resolving these references is essential for accurately extracting obligations and rights from legal documents.
Knowledge Graph Construction Building knowledge graphs from text requires merging all mentions of the same entity. Without coreference resolution, each mention would be treated as a separate entity, producing a fragmented and inaccurate knowledge graph.
Coreference Resolution in Southeast Asian Languages
Coreference resolution presents unique challenges across Southeast Asian languages:
- Pro-drop languages: Thai, Vietnamese, and other ASEAN languages frequently omit pronouns entirely when the referent is contextually obvious. This requires systems to infer implied references
- Honorific systems: Thai, Malay, and other languages use elaborate pronoun and title systems that vary by social relationship. "Khun," "Pee," and "Nong" in Thai carry coreference information that systems must understand
- Classifier systems: Languages like Thai and Vietnamese use classifiers that can serve as anaphoric references, adding complexity to coreference tracking
- Limited training data: Coreference resolution requires annotated data showing which mentions refer to which entities. Such datasets are scarce for most Southeast Asian languages
Challenges and Limitations
Coreference resolution remains one of the hardest problems in NLP:
- World knowledge: Resolving "The trophy would not fit in the suitcase because it was too big" requires knowing that trophies are typically the thing that is too big, not suitcases. Such commonsense reasoning is difficult for machines
- Ambiguity: Some references are genuinely ambiguous even for humans, and systems must handle these cases gracefully
- Long-distance references: When mentions are far apart in a document, maintaining context for accurate resolution becomes challenging
- Cross-document coreference: Tracking entities across multiple documents is significantly harder than within a single document
Getting Started with Coreference Resolution
- Assess impact — Determine where coreference errors are degrading the quality of your current NLP applications
- Evaluate tools — Libraries like spaCy (with neuralcoref), Stanford CoreNLP, and Hugging Face models offer coreference resolution capabilities
- Test on your data — Coreference performance varies significantly by domain and language. Test on representative samples of your actual business text
- Integrate into pipelines — Coreference resolution is most valuable as a component within larger NLP pipelines for information extraction, summarization, and conversation understanding
- Monitor accuracy — Track resolution accuracy over time and retrain models as your document types and language patterns evolve
Coreference resolution may be one of the least visible NLP capabilities, but its impact on AI system quality is substantial. Every time your document analysis tool correctly attributes an obligation to the right party in a contract, or your chatbot correctly tracks what a customer is referring to across multiple messages, coreference resolution is working behind the scenes. When it fails, your systems make fundamental errors — attributing the wrong action to the wrong entity.
For CEOs, the business impact is most visible in customer experience and operational accuracy. AI systems that cannot track references across a conversation frustrate customers who must repeat themselves. Document processing systems that confuse entity references produce errors in compliance, legal, and financial workflows.
For CTOs building NLP pipelines, coreference resolution is a critical quality multiplier. Investing in accurate coreference resolution improves the output of every downstream application — information extraction, summarization, question answering, and conversation systems. In Southeast Asian markets, where languages use complex pronoun systems and frequently drop subjects, this investment is even more important for achieving accurate multilingual NLP.
- Evaluate coreference resolution quality as part of your overall NLP platform assessment, as it directly affects the accuracy of information extraction, summarization, and dialogue systems
- Test coreference resolution on multi-turn conversations and long documents, not just isolated sentences, as real-world performance often degrades with text length and complexity
- Consider the impact of pro-drop language patterns in Southeast Asian languages, where subjects are frequently omitted and must be inferred for accurate entity tracking
- Implement coreference resolution as a pipeline component rather than a standalone feature, ensuring its output feeds into downstream applications like knowledge graphs and document analysis
- Budget for domain-specific evaluation and potential model fine-tuning, as general-purpose coreference models may struggle with specialized terminology and reference patterns in your industry
- Monitor for cases where coreference errors propagate through your NLP pipeline, causing compounding mistakes in downstream applications
Frequently Asked Questions
Why is coreference resolution important for chatbots?
Chatbots must track what users are referring to across multiple messages. When a customer says "I want to return the blue shirt I ordered last week. How long will it take to get a refund for it?" the chatbot needs to understand that "it" refers to "the blue shirt." Without coreference resolution, the bot might lose track of what the customer is discussing, forcing them to repeat information and creating a frustrating experience that damages customer satisfaction.
How accurate is coreference resolution today?
State-of-the-art coreference resolution systems achieve around 80 to 85 percent accuracy on English language benchmarks, which represents significant progress but still leaves room for errors. Performance is generally lower for informal text, longer documents, and less-resourced languages. For business applications, this means human review is still advisable for high-stakes document analysis. Accuracy continues to improve as large language models provide better contextual understanding.
More Questions
Cross-lingual coreference resolution — tracking entity references across different languages within or across documents — is an active area of research. Some multilingual models can handle this to a degree, but accuracy is lower than monolingual resolution. For Southeast Asian businesses dealing with mixed-language documents and conversations, the most practical approach is currently to resolve coreferences within each language segment and then merge results using entity linking techniques.
Need help implementing Coreference Resolution?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how coreference resolution fits into your AI roadmap.