Collect annotated text
Assemble documents with coreference annotations (mention spans and cluster ids) and include edge cases such as nested mentions and long-distance references.
01 / THE IDEA
Coreference Resolution in practice.
Coreference resolution groups expressions that refer to the same entity (for example, 'Alice', 'she', 'the engineer') so downstream systems can reason about entities consistently. Common errors come from pronoun ambiguity, nested mentions, and long-distance antecedents; evaluation typically measures mention detection plus clustering precision and recall.
Real-world use case · Proposed extension
A proposed application is a document-summarisation assistant that collapses mentions of the same entity to a canonical name before generating summaries. This extension is not implemented in the supplied starter code.
02 / THE BUILD
How to build a coreference resolution system project
step by step.
Build a baseline model
Start with a mention-detection component and a simple pairwise classifier (feature-based or transformer embeddings) that predicts whether two mentions corefer.
Evaluate clustering metrics
Measure mention-level precision/recall and cluster-level metrics such as MUC, B3 and CEAF, and inspect failure modes like pronoun chains and appositives.
Plan a deployable pipeline
Wrap mention detection and clustering behind an API, add human-in-the-loop review for uncertain clusters, and monitor drift in mention distributions.
03 / SOURCE SNAPSHOT
What the coreference resolution system starter actually does.
The supplied starter implements a small text-classification baseline: it trains a TF-IDF (1–2 gram) + LogisticRegression pipeline on six short, hand-written sentences with binary labels, then prints a classification report and a sample prediction. It does not implement mention detection, mention-pair scoring, clustering, or any coreference-specific data structures.
SOURCE EXCERPTPython
model = Pipeline(
steps=[
("tfidf", TfidfVectorizer(ngram_range=(1, 2), min_df=1)),
("classifier", LogisticRegression(max_iter=1000)),
]
)
Important limitation
The code contains no coreference model or annotated coreference data: there are only six independent sentences with binary labels, and the pipeline is a TF-IDF + LogisticRegression text classifier rather than any mention-detection, pairwise scoring or clustering component required for coreference resolution.
04 / BEYOND THE CLASSROOM
Where a coreference resolution system project could go next.
PROPOSED BUSINESS CONCEPT
Document-level coreference pilot
A proposed extension: replace the starter data with a public coreference dataset (for example, OntoNotes) and implement a mention-detection stage plus a pairwise scoring model that clusters mentions into entity chains.
WHO IT COULD SERVETeams building document understanding tools
FIRST MVPA pipeline that detects mentions, scores mention pairs with contextual embeddings, and outputs clustered entity chains with a simple UI for human review.
KEY RISKCoreference errors can misattribute information between entities; keep a human reviewer in the loop and do not use early prototypes for clinical, legal or safety-critical decisions.
This is a potential application pathway, not a delivered product. The supplied starter does not implement this service or validate it with real users.
05 / WHAT YOU TAKE AWAY
What a coreference resolution system project puts on your resume.
- Explain the coreference-resolution task and common error cases such as pronoun ambiguity and nested mentions.
- Prepare an annotated dataset suitable for mention detection and clustering, and split it for evaluation.
- Build a baseline mention-pair classifier using contextual features and cluster mentions into entity chains.
- Evaluate with clustering metrics (MUC, B3, CEAF) and inspect qualitative errors such as long-distance antecedents.
Want to build this with mentors and a lab?
AI Engineer Course Networkers Home runs this topic inside its AI Engineer Course, with placement support and hands-on labs.
06 / QUESTIONS STUDENTS ASK
Coreference Resolution System project FAQs.
- Is this suitable as a final year project?
- Yes — coreference resolution is a substantial NLP problem with scope for research and engineering, suitable for a final year project if you expand beyond the starter baseline into mention detection and clustering.
- What skills and tools will I need?
- You should know Python, NLP libraries (spaCy, scikit-learn) and basics of feature engineering and clustering; familiarity with neural models and annotated coreference datasets will help for stronger results.
- How should I evaluate progress and how long will it take?
- Evaluate mention detection and clustering with precision, recall and CoNLL-style metrics; a prototype with rule-based or pairwise models can take 6–10 weeks, while learning neural approaches may take longer.