https://github.com/tharindudr/simple-sentence-similarity

Exploring the simple sentence similarity measurements using word embeddings
https://github.com/tharindudr/simple-sentence-similarity

elmo fasttext glove ipynb python sentence-embeddings sentence-similarity wmd word-embeddings word2vec

Last synced: about 1 month ago
JSON representation

Exploring the simple sentence similarity measurements using word embeddings

• Host: GitHub
• URL: https://github.com/tharindudr/simple-sentence-similarity
• Owner: TharinduDR
• License: apache-2.0
• Created: 2018-11-07T14:08:46.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2024-08-20T16:55:33.000Z (3 months ago)
• Last Synced: 2024-09-27T23:41:01.032Z (about 2 months ago)
• Topics: elmo, fasttext, glove, ipynb, python, sentence-embeddings, sentence-similarity, wmd, word-embeddings, word2vec
• Language: Python
• Homepage:
• Size: 60.4 MB
• Stars: 100
• Watchers: 9
• Forks: 37
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Downloads](https://pepy.tech/badge/simplests)](https://pepy.tech/project/simplests)

#  Simple Sentence Similarity

We provide a collection of simple unsupervised semantic textual similarity methods to calculate semantic similarity between two sentences.

### References

If you find this code useful in your research, please consider citing:

```

@inproceedings{ranasinghe-etal-2019-enhancing,

    title = "Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations",

    author = "Ranasinghe, Tharindu  and

      Orasan, Constantin  and

      Mitkov, Ruslan",

    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",

    month = sep,

    year = "2019",

    address = "Varna, Bulgaria",

    publisher = "INCOMA Ltd.",

    url = "https://www.aclweb.org/anthology/R19-1115",

    doi = "10.26615/978-954-452-056-4_115",

    pages = "994--1003",

    abstract = "Calculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. All modern state of the art STS methods rely on word embeddings one way or another. The recently introduced contextualised word embeddings have proved more effective than standard word embeddings in many natural language processing tasks. This paper evaluates the impact of several contextualised word embeddings on unsupervised STS methods and compares it with the existing supervised/unsupervised STS methods for different datasets in different languages and different domains",

}

}

```