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A curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.
| Date | Stars |
|---|---|
| 2026-07-24 | 368 |
| 2026-07-25 | 368 |
| 2026-07-28 | 368 |
| 2026-07-30 | 368 |
| 2026-07-31 | 367 |
| 2026-08-06 | 367 |
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# Awesome Semantic-Search [](https://awesome.re) [](https://conventionalcommits.org)
<img src ="logo.svg" />
Logo made by [@createdbytango](https://instagram.com/createdbytango).
**Looking for More Paper Additions.
PS: Raise a PR**
Following repository aims to serve a meta-repository for [Semantic Search](https://en.wikipedia.org/wiki/Semantic_search) and [Semantic Similarity](http://nlpprogress.com/english/semantic_textual_similarity.html) related tasks.
Semantic Search isn't limited to text! It can be done with images, speech, etc.There are numerous different use-cases and applications of semantic search.
Feel free to raise a PR on this repo!
## Contents
- [Papers](#papers)
- [2014](#2014)
- [2015](#2015)
- [2016](#2016)
- [2017](#2017)
- [2018](#2018)
- [2019](#2019)
- [2020](#2020)
- [2021](#2021)
- [2022](#2022)
- [2023](#2023)
- [Articles](#articles)
- [Libraries and Tools](#libraries-and-tools)
- [Datasets](#datasets)
- [Milestones](#milestones)
## Papers
### 2010
- [Priority Range Trees](https://arxiv.org/abs/1009.3527)
- [Information Retrieval and the semantic web](https://ieeexplore.ieee.org/document/5607549) 📄
### 2014
- [A Latent Semantic Model with Convolutional-Pooling
Structure for Information Retrieval](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2014_cdssm_final.pdf) 📄
### 2015
- [Skip-Thought Vectors](https://arxiv.org/pdf/1506.06726.pdf) 📄
- [Practical and Optimal LSH for Angular Distance](https://proceedings.neurips.cc/paper/2015/hash/2823f4797102ce1a1aec05359cc16dd9-Abstract.html)
### 2016
- [Bag of Tricks for Efficient Text Classification](https://arxiv.org/abs/1607.01759) 📄
- [Enriching Word Vectors with Subword Information](https://arxiv.org/abs/1607.04606) 📄
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://arxiv.org/abs/1603.09320)
- [On Approximately Searching for Similar Word Embeddings](https://www.aclweb.org/anthology/P16-1214.pdf)
- [Learning Distributed Representations of Sentences from Unlabelled Data](https://arxiv.org/abs/1602.03483)📄
- [Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement](https://arxiv.org/abs/1610.02455)
### 2017
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://research.fb.com/wp-content/uploads/2017/09/emnlp2017.pdf) 📄
- [Semantic Textual Similarity For Hindi](https://www.semanticscholar.org/paper/Semantic-Textual-Similarity-For-Hindi-Mujadia-Mamidi/372f615ce36d7543512b8e40d6de51d17f316e0b)📄
- [Efficient Natural Language Response Suggestion for Smart Reply](https://arxiv.org/abs/1705.00652)📃
### 2018
- [Universal Sentence Encoder](https://arxiv.org/pdf/1803.11175.pdf) 📄
- [Learning Semantic Textual Similarity from Conversations](https://arxiv.org/pdf/1804.07754.pdf) 📄
- [Google AI Blog: Advances in Semantic Textual Similarity](https://ai.googleblog.com/2018/05/advances-in-semantic-textual-similarity.html) 📄
- [Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech](https://arxiv.org/abs/1803.08976))🔊
- [Optimization of Indexing Based on k-Nearest Neighbor Graph for Proximity Search in High-dimensional Data](https://arxiv.org/abs/1810.07355) 🔊
- [Fast Approximate Nearest Neighbor Search With The
Navigating Spreading-out Graph](http://www.vldb.org/pvldb/vol12/p461-fu.pdf)
- [The Case for Learned Index Structures](https://dl.acm.org/doi/10.1145/3183713.3196909)
### 2019
- [LASER: Language Agnostic Sentence Representations](https://engineering.fb.com/2019/01/22/ai-research/laser-multilingual-sentence-embeddings/) 📄
- [Document Expansion by Query Prediction](https://arxiv.org/abs/1904.08375) 📄
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-NetExcerpt of 16,781 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3e5f3b40e588cc29, topic:awesome, topic:awesome-list, desc:curated list
matched fp:3e5f3b40e588cc29, topic:semantic-search, readme:nearest neighbor
matched fp:3e5f3b40e588cc29, topic:sentence-embeddings, readme:sentence embeddings
matched fp:3e5f3b40e588cc29, topic:nlp, readme:text classification