Search Results for author: Sadid A. Hasan

Found 17 papers, 2 papers with code

Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions

no code implementations INLG (ACL) 2020 David M. Howcroft, Anya Belz, Miruna-Adriana Clinciu, Dimitra Gkatzia, Sadid A. Hasan, Saad Mahamood, Simon Mille, Emiel van Miltenburg, Sashank Santhanam, Verena Rieser

Human assessment remains the most trusted form of evaluation in NLG, but highly diverse approaches and a proliferation of different quality criteria used by researchers make it difficult to compare results and draw conclusions across papers, with adverse implications for meta-evaluation and reproducibility.

Experimental Design

An Ontology-driven Treatment Article Retrieval System for Precision Oncology

no code implementations13 Feb 2020 Zheng Chen, Sadid A. Hasan, Joey Liu, Vivek Datla, Md Shamsuzzaman, Hafiz Khan, Mohammad S. Sorower, Gabe Mankovich, Rob van Ommering, Nevenka Dimitrova

This paper presents an ontology-driven treatment article retrieval system developed and experimented using the data and ground truths provided by the TREC 2017 precision medicine track.


SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks

1 code implementation27 Jan 2020 Lucas Emanuel Silva e Oliveira, Ana Carolina Peters, Adalniza Moura Pucca da Silva, Caroline P. Gebeluca, Yohan Bonescki Gumiel, Lilian Mie Mukai Cintho, Deborah Ribeiro Carvalho, Sadid A. Hasan, Claudia Maria Cabral Moro

The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and evaluation of natural language processing (NLP) algorithms.

DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference

no code implementations NAACL 2018 Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Z. Fern, Oladimeji Farri

Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference.

Natural Language Inference

Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning

no code implementations IJCNLP 2017 Yuan Ling, Sadid A. Hasan, Vivek Datla, Ashequl Qadir, Kathy Lee, Joey Liu, Oladimeji Farri

Clinical diagnosis is a critical and non-trivial aspect of patient care which often requires significant medical research and investigation based on an underlying clinical scenario.

Decision Making reinforcement-learning +1

Condensed Memory Networks for Clinical Diagnostic Inferencing

no code implementations6 Dec 2016 Aaditya Prakash, Siyuan Zhao, Sadid A. Hasan, Vivek Datla, Kathy Lee, Ashequl Qadir, Joey Liu, Oladimeji Farri

We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory.

Neural Clinical Paraphrase Generation with Attention

no code implementations WS 2016 Sadid A. Hasan, Bo Liu, Joey Liu, Ashequl Qadir, Kathy Lee, Vivek Datla, Aaditya Prakash, Oladimeji Farri

Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact.

Document Summarization Information Retrieval +5

Complex Question Answering: Unsupervised Learning Approaches and Experiments

no code implementations15 Jan 2014 Yllias Chali, Shafiq Rayhan Joty, Sadid A. Hasan

Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization where the goal is to produce a single text as a compressed version of a set of documents with a minimum loss of relevant information.

Document Summarization Multi-Document Summarization +1

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