NLP-Project-2023

STS: Semantic Textual Similarity

Website with data: http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark
Dataset: The dataset consists of pairs of sentences that have been judged by humans on how similar they are to one another. Here the similarity indicates the level of (semantic) agreement between the meaning of the sentences. The sentences have been sourced from three domains (news, caption and forum).
Task: the original task here is to predict the similarity between the sentences. Note that this problem could be handled as either a regression (predict a real score) or classification (predict similar/non-similar label) task.
Hints and suggestions: You could investigate various unsupervised techniques, such as computing the cosine similarity between TF-IDF bag-of-words vectors for the sentences, extending the BOW vector to include n-grams (bigrams and trigrams), and also consider the distance between average word embeddings. Moreover, supervised models could be trained, in particular transformer-based models are often fine-tuned to classify pairs of sentences.

Name		Name	Last commit message	Last commit date
Latest commit History 58 Commits
.ipynb_checkpoints		.ipynb_checkpoints
Dataset		Dataset
.gitignore		.gitignore
README.md		README.md
data.txt		data.txt
results.csv		results.csv
semantic-textual-similarity.ipynb		semantic-textual-similarity.ipynb
semantic_search.ipynb		semantic_search.ipynb
text-clustering.ipynb		text-clustering.ipynb
tfidf-char-wb.ipynb		tfidf-char-wb.ipynb
tfidf.ipynb		tfidf.ipynb
transformers.ipynb		transformers.ipynb
universal-sentence-encoder.ipynb		universal-sentence-encoder.ipynb
word-embedding.ipynb		word-embedding.ipynb
word2vec-regression.ipynb		word2vec-regression.ipynb