ml6team

🔑 Keyphrase Extraction Model: KBIR-inspec Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time ⏳. Here is where Artificial Intelligence 🤖 comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text. 📓 Model Description This model uses KBIR as its base model and fine-tunes it on the Inspec dataset. KBIR or Keyphrase Boundary Infilling with Replacement is a pre-trained model which utilizes a multi-task learning setup for optimizing a combined loss of Masked Language Modeling (MLM), Keyphrase Boundary Infilling (KBI) and Keyphrase Replacement Classification (KRC). You can find more information about the architecture in this paper. Keyphrase extraction models are transformer models fine-tuned as a token classification problem where each word in the document is classified as being part of a keyphrase or not. | Label | Description | | ----- | ------------------------------- | | B-KEY | At the beginning of a keyphrase | | I-KEY | Inside a keyphrase | | O | Outside a keyphrase | Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021). Sahrawat, Dhruva, Debanjan Mahata, Haimin Zhang, Mayank Kulkarni, Agniv Sharma, Rakesh Gosangi, Amanda Stent, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann. "Keyphrase extraction as sequence labeling using contextualized embeddings." In European Conference on Information Retrieval, pp. 328-335. Springer, Cham, 2020. ✋ Intended Uses & Limitations 🛑 Limitations This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out. Only works for English documents. 📚 Training Dataset Inspec is a keyphrase extraction/generation dataset consisting of 2000 English scientific papers from the scientific domains of Computers and Control and Information Technology published between 1998 to 2002. The keyphrases are annotated by professional indexers or editors. | Parameter | Value | | --------- | ------| | Learning Rate | 1e-4 | | Epochs | 50 | | Early Stopping Patience | 3 | Preprocessing The documents in the dataset are already preprocessed into list of words with the corresponding labels. The only thing that must be done is tokenization and the realignment of the labels so that they correspond with the right subword tokens. Postprocessing (Without Pipeline Function) If you do not use the pipeline function, you must filter out the B and I labeled tokens. Each B and I will then be merged into a keyphrase. Finally, you need to strip the keyphrases to make sure all unnecessary spaces have been removed. Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases. The model achieves the following results on the Inspec test set: | Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M | |:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:| | Inspec Test Set | 0.53 | 0.47 | 0.46 | 0.36 | 0.58 | 0.41 | 0.58 | 0.60 | 0.56 | 🚨 Issues Please feel free to start discussions in the Community Tab.