Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation

This repository houses the codebase for replicating the experiments detailed in Jorge Martinez-Gil's paper on Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation. Discover more insights and applications through our arXiv preprint and an accessible Medium article.

🌍 Overview

Word sense disambiguation (WSD) plays a pivotal role in Natural Language Processing (NLP). It involves deciphering the intended meaning of a word in a multi-sense context, which is crucial for enhancing the performance of applications like machine translation and information retrieval.

Our repository offers an innovative unsupervised approach to WSD using context-aware semantic similarity:

1. Preprocessing: Clean and prepare your text data.
2. Context Extraction: Identify the context surrounding the ambiguous word.
3. Semantic Similarity: Utilize pre-trained sentence embeddings and cosine similarity to evaluate semantic parallels.
4. Sense Selection: Choose the sense with the highest similarity score.

Included are the necessary code, pre-trained embeddings, and test data for thorough evaluation.

🛠️ Installation

pip install -r requirements.txt

📊 Dataset

The CoarseWSD-20 dataset, a well-known resource for coarse-grained WSD, forms the backbone of our experiments. It encompasses 20 commonly ambiguous words.

🚀 Usage Guide

Follow these steps to apply our method:

1. Clone this repository.
2. Install dependencies (refer to the installation section).
3. Download and position pre-trained word embeddings in the data directory.
4. Execute the script of your choice and observe the results in your console.

📝 Evaluation

Evaluate our approach using the provided test data.

Unsupervised Word Sense Disambiguation (UWSD):

• python uwsd_bert.py - BERT
• python uwsd_elmo.py - ELMo
• python uwsd_use.py - Universal Sentence Encoder (USE)
• python uwsd_wmd.py - Word Mover's Distance (WMD)

Context-Aware Semantic Similarity (CASS):

• python cass-wordnet+bert.py - CASS using WordNet and BERT
• python cass-word2vec+bert.py - CASS using word2vec and BERT
• python cass-webscrapping+bert.py - CASS using webscraping and BERT

Example Scenario UWSD:

• Typed object-oriented programming languages, such as java and c++ , often do not support first-class methods --> options (island, programming language)
  • uwsd_bert: programming language
  • uwsd_elmo: programming language
  • uwsd_use: programming language
  • uwsd_wmd: programming language
  • ChatGPT-4: programming language

Example Scenario CASS:

• Vienna is a nice city situated in the center of the European continent.
  • cass-wordnet+bert: middle
  • cass-word2vec+bert: hub
  • cass-webscrapping+bert: mid
  • ChatGPT-4: middle

📈 Performance Results

The summary of the results in terms of the CoarseWSD-20 dataset disambiguation is:

Strategy	Hits	Accuracy
UWSD+BERT	7,927	77.74%
MFS-Baseline	7,487	73.43%
UWSD+USE	7,335	71.94%
UWSD+ELMo	7,010	68.75%
UWSD+WMD	5,868	57.55%
RO-Baseline	4,459	43.73%

📚 Citation

If you utilize our work, kindly cite us:

@inproceedings{martinez2023b,
  author    = {Jorge Martinez-Gil},
  title     = {Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation},
  journal   = {CoRR},
  volume    = {abs/2305.03520},
  year      = {2023},
  url       = {https://arxiv.org/abs/2305.03520},
  doi       = {https://doi.org/10.48550/arXiv.2305.03520},
  eprinttype = {arXiv},
  eprint    = {2305.03520}
}

📄 License

Released under the MIT License. View License.