Create a sense-labelled corpus
Choose a sense inventory (e.g. WordNet), then label sentences with the intended sense for target words or use an existing sense-labelled dataset.
01 / THE IDEA
Word Sense Disambiguation in practice.
Word sense disambiguation (WSD) assigns the correct meaning (sense) to an ambiguous word given its context. Practical WSD requires sense inventories, sense-labelled training data, careful context features (lexical windows or contextual embeddings) and qualitative inspection of confusable senses.
Real-world use case · Proposed extension
A proposed application: augment a search or question-answering system to return results ranked by the sense a user likely intended. This extension requires a sense inventory and labelled data and is not implemented in the supplied starter.
02 / THE BUILD
How to build a word sense disambiguation project
step by step.
Build a baseline model
Train a compact baseline using TF-IDF or contextual embeddings and a multiclass classifier to predict sense IDs from the surrounding context.
Evaluate with per-sense metrics
Report macro F1 and per-sense precision/recall, and inspect frequent confusions between similar senses.
Prepare a reviewable output
Provide example predictions with context and the predicted sense definition so human reviewers can correct and augment training data.
03 / SOURCE SNAPSHOT
What the word sense disambiguation starter actually does.
The supplied starter uses six short sentences with binary labels, splits them into train and test sets, fits a Pipeline with TfidfVectorizer and LogisticRegression, prints a classification report, and shows one sample prediction. It does not use any sense inventory or sense-tagged examples.
SOURCE EXCERPTPython
model = Pipeline(
steps=[
("tfidf", TfidfVectorizer(ngram_range=(1, 2), min_df=1)),
("classifier", LogisticRegression(max_iter=1000)),
]
)
model.fit(X_train, y_train)Important limitation
The code contains no sense-tagged dataset or sense identifiers and therefore does not perform word sense disambiguation; its labels are simple binary tags unrelated to word senses.
04 / BEYOND THE CLASSROOM
Where a word sense disambiguation project could go next.
PROPOSED BUSINESS CONCEPT
WSD pilot with WordNet senses
Proposed extension: assemble a small sense-labelled dataset for a few ambiguous lemmas (using WordNet sense IDs), train a multiclass classifier with contextual embeddings, and surface example predictions for human review.
WHO IT COULD SERVESearch platforms or digital libraries
FIRST MVPA small API that accepts a sentence and target word, returns the predicted sense ID, sense definition, and supporting context examples for human validation.
KEY RISKWSD errors can mislead downstream search or annotation workflows; keep a human reviewer in the loop and do not use this prototype for critical automation without extensive evaluation.
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 word sense disambiguation project puts on your resume.
- Assemble or convert a sense-labelled corpus using a chosen inventory such as WordNet.
- Train a multiclass classifier that predicts sense IDs from contextual features or embeddings.
- Report macro F1 and per-sense confusion matrices and inspect frequent confusions.
- Explain the starter limitation: it uses binary labels on six sentences and has no sense annotations.
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
Word Sense Disambiguation project FAQs.
- Is this suitable as a final year project?
- Yes — WSD is a solid NLP applied project that combines linguistic analysis and machine learning; expect to design sense inventories, collect or create sense‑tagged data, and evaluate performance.
- What skills and tools will I need to implement WSD?
- Familiarity with Python, NLP libraries (spaCy, scikit‑learn), word embeddings or contextual models, dataset annotation concepts, and evaluation metrics will be important.
- How should I evaluate progress and how long will it take?
- Start by selecting a sense inventory and a small sense‑tagged corpus, then iterate on context features and classifiers; a basic prototype can take 4–8 weeks, deeper work (datasets/analysis) longer.