NLP-Measuring semantic sentence similarity using baseline and neural models

1. Pre-Requisites

• Python Editor (eg: Pycharm , Anaconda) with Python 3.

• Jupyter Notebook

• Pytorch (latest version) , GPU not necessary

• Working knowledge of Machine Learning, Deep Learning and NLP to start with

• Packages like NLTK, sklearn, Numpy, Pandas, Scipy, Sent2vec

• BioSentVec embedding for 4BioSentvec.ipynb is available in https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/BioSentVec/BioSentVec_PubMed_MIMICIII-bigram_d700.bin

• Bio Word2Vec embedding for 3Word2Vec.ipynb is available in https://bio.nlplab.org

• The Dataset can be requested from https://portal.dbmi.hms.harvard.edu and https://sites.google.com/view/ohnlp2018/home(Task 2)

Research papers:

[1] Wang Y, Afzal N, Liu S, Rastegar-Mojarad M, Wang L, Shen F, Fu S, Liu H. Overview of the BioCreative/OHNLP Challenge 2018 Task 2: Clinical Semantic Textual Similarity. Proceedings of the BioCreative/OHNLP Challenge. 2018.

[2] Wang Y, Afzal N, Fu S, Wang L, Shen F, Rastegar-Mojarad M, Liu H. MedSTS: a resource for clinical semantic textual similarity. Language Resources and Evaluation. 2018, Jan 1:1-6.

[3] Chen Q, Peng Y, Lu Z. BioSentVec: creating sentence embeddings for biomedical texts. In: The 7th IEEE international conference on healthcare informatics; 2019.