Saurabh Kulshreshtha

Saurabh Kulshreshtha

S. Kulshreshtha J.L. Redondo-García C.Y. Chang Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study. SigTYP, EMNLP 2020. 
@inproceedings{kulshreshtha2020cross,
      title={Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study},
      author={Kulshreshtha, Saurabh and Garcia, Jose Luis Redondo and Chang, Ching Yun},
      booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings},
      pages={933--942},
      year={2020}}
A. Rumshisky M. Gronas P. Potash M. Dubov A. Romanov S. Kulshreshtha A. Gribov Combining Network and Language Indicators for Tracking Conflict Intensity. Proceedings of SocInfo 2017. Oxford, United Kingdom. 
@inproceedings{rumshisky_combining_2017,
      author = “Rumshisky, Anna and Gronas, Mikhail and Potash, Peter and Dubov, Mikhail and Romanov, Alexey and Kulshreshtha, Saurabh and Gribov, Alex”,
      editor = “Ciampaglia, Giovanni Luca and Mashhadi, Afra and Yasseri, Taha”,
      address = “Cham”,
      title = “Combining {Network} and {Language} {Indicators} for {Tracking} {Conflict} {Intensity}",
      isbn = “978-3-319-67256-4”,
      booktitle = “Social {Informatics}",
      publisher = “Springer International Publishing”,
      year = “2017”,
      pages = “391–404”
      }
W. Boag E. Sergeeva S. Kulshreshtha P. Szolovits A. Rumshisky T. Naumann CliNER 2.0: Accessible and Accurate Clinical Concept Extraction. ML4H: Machine learning for Health Workshop at NIPS 2017. Long Beach, CA. 
@article{boag2018cliner,
      title={CliNER 2.0: Accessible and Accurate Clinical Concept Extraction},
      author={Boag, Willie and Sergeeva, Elena and Kulshreshtha, Saurabh and Szolovits, Peter and Rumshisky, Anna and Naumann, Tristan},
      year={2018}
      }

  • CliNER: A Clinical Named Entity Recognition system

    Clinical Named Entity Recognition system (CliNER) is an open-source natural language processing system for named entity recognition in clinical text of electronic health records. It supports:

    1. a traditional machine learning architecture for named entity recognition with a CRF classifier
    2. a deep learning architecture using a recurrent neural network with LSTM for sequence labelling

  • Conflict, bias, and sentiment in social media

    This project develops methods for modeling, detecting and measuring civil conflict as reflected in social media, as well as the associated informational biases in the news media. The goal is to combine different measures of verbal and non-verbal user behavior and the associated network-scale effects in order to track conflict development over time.

    We view the problems of conflict and bias detection as inherently intertwined, since conflicts often lead to biased interpretations on both sides. The goal is to evaluate the hypothesis that identifying user communities divided with respect to a set of polarizing issues will allow us to characterize relevant information sources in terms of ideological biases propagated by the opposing sides.