Similarity search and deduplication at scale

76 points by dsalaj 3 years ago | 16 comments

dsalaj 3 years ago |

I have been working on an entity matching solution for two years now, and I have decided to write down some of the learning I picked up along the way. Turns out there are too many relevant details to cover in a single post, so I will cover the topic in multiple parts.

This first part is the high-level introduction, useful for project planning and architecture decisions that need to be made early in the development process. Any feedback is welcome, along with wishes for the follow-up parts if you have something specific that you would like to be covered.

sireat 3 years ago | |

Thank you for a very helpful writeup!

Do you have any materials on word embedding strategies past Word2Vec? BERT and beyond?

I am currently working on a recommendation engine for a large library - original idea being to find "similar" documents - the funding comes from a plagiarism checking project.

I was slightly surprised how deceptively simple the widely cited winnowing paper is https://dl.acm.org/doi/10.1145/872757.872770 . The key idea being simple mod reduction of hashed fingerprints.

My project's goal is to find phrase level similarities to assist researchers.

It seems k-grams, n-grams, tf-idf and even Word2Vec is not going to cut it. A "smarter" context aware embedding is in order. My foray in training BERT from scratch was not very successful. - My corpora are not in English...

PS. As usual I spend most of the time on improving OCR quality and preprocessing corpora...

dsalaj 3 years ago | | |

For achieving a high accuracy for matching, it really comes down to details of your specific domain and dataset. Regarding the attempt of using large pre-trained language models to be able to find semantically similar documents, which is what you are attempting now, maybe try Whisper or other multilingual models, and then fine tune them on your dataset.

But a better bet might be actually turning looking into simpler embedding and methods and attempting to directly improve them by including some domain knowledge in the method or the process. Again, it is hard to judge what might work better just looking at the surface.

In case you really need to work with labeled datasets, set up a strong baseline, look into the active-learning methods and set up the loop, do a few iterations and try to predict if it will scale sufficiently fast to your target accuracy.

nerdponx 3 years ago | |

Thank you for this writeup. Having done some work on deduplication/matching systems, my experience (as with many things in data science) is that there are a lot of things consider and there is no single best solution. Hopefully you are able to keep up with this series, because I think it will be very helpful to many people.

fzliu 3 years ago |

I'm surprised to see that ML-based semantic search is barely touched on in this article. There's a strong focus on entity matching, but an arguably more powerful way to conduct similarity search is to leverage embedding vectors from trained models.

A great upside to this approach is that it works for a variety of different types of unstructured data (images, video, molecular structures, geospatial data, etc), not just text. The rise of multimodal models such as CLIP (https://openai.com/blog/clip) makes this even more relevant today. Combine it with a vector database such as Milvus (https://milvus.io) and you'll be able to do this at scale with very minimal effort.

saintarian 3 years ago | |

Shameless plug - for folks who don't want to take on the work of model selection, on-demand scaling of model serving, scaling the vector database for search set size and query throughput, we built a service that hides all this behind a simple API [1]. The example in [1] is for images, but here is quick-start for text [2].

[1] https://www.nyckel.com/semantic-image-search [2] https://www.nyckel.com/docs/text-search-quickstart

marginalia_nu 3 years ago | |

Maybe from ... "at scale" bit? ML approaches are relatively computationally expensive.

HyperSane 3 years ago | | |

And opaque and hard to modify.

HyperSane 3 years ago | |

The latent spaces created by neural networks inherently de-dupe data.

dang 3 years ago |

I would like to know if any of these techniques could be used for identifying articles that are either copies of each other, or near-copies, or different articles on the same story.