Search Results for author: Karin Verspoor

Found 54 papers, 14 papers with code

Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration

no code implementations EMNLP (NLP-COVID19) 2020 Yulia Otmakhova, Karin Verspoor, Timothy Baldwin, Simon Šuster

Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose.

Topic Models

Learning from Unlabelled Data for Clinical Semantic Textual Similarity

no code implementations EMNLP (ClinicalNLP) 2020 Yuxia Wang, Karin Verspoor, Timothy Baldwin

Domain pretraining followed by task fine-tuning has become the standard paradigm for NLP tasks, but requires in-domain labelled data for task fine-tuning.

Semantic Textual Similarity

Using Discourse Structure to Differentiate Focus Entities from Background Entities in Scientific Literature

no code implementations ALTA 2021 Antonio Jimeno Yepes, Ameer Albahem, Karin Verspoor

In developing systems to identify focus entities in scientific literature, we face the problem of discriminating key entities of interest from other potentially relevant entities of the same type mentioned in the articles.

The patient is more dead than alive: exploring the current state of the multi-document summarisation of the biomedical literature

no code implementations ACL 2022 Yulia Otmakhova, Karin Verspoor, Timothy Baldwin, Jey Han Lau

Although multi-document summarisation (MDS) of the biomedical literature is a highly valuable task that has recently attracted substantial interest, evaluation of the quality of biomedical summaries lacks consistency and transparency.

Improving negation detection with negation-focused pre-training

no code implementations9 May 2022 Thinh Hung Truong, Timothy Baldwin, Trevor Cohn, Karin Verspoor

Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text.

Data Augmentation Negation Detection

ITTC @ TREC 2021 Clinical Trials Track

no code implementations16 Feb 2022 Thinh Hung Truong, Yulia Otmakhova, Rahmad Mahendra, Timothy Baldwin, Jey Han Lau, Trevor Cohn, Lawrence Cavedon, Damiano Spina, Karin Verspoor

This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the TREC 2021 Clinical Trials Track.

MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival Risk

1 code implementation2 Feb 2022 Gourab Ghosh Roy, Nicholas Geard, Karin Verspoor, Shan He

We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that are important in risk prediction for particular cancer types, is reliable.

Survival Analysis

Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT

1 code implementation6 Jan 2022 Aparna Elangovan, Yuan Li, Douglas E. V. Pires, Melissa J. Davis, Karin Verspoor

However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision.

Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation

no code implementations EACL 2021 Aparna Elangovan, Jiayuan He, Karin Verspoor

Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP).

Named Entity Recognition Relation Extraction

Memorization vs. Generalization: Quantifying Data Leakage in NLP Performance Evaluation

1 code implementation3 Feb 2021 Aparna Elangovan, Jiayuan He, Karin Verspoor

Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP).

Named Entity Recognition Relation Extraction

Assigning function to protein-protein interactions: a weakly supervised BioBERT based approach using PubMed abstracts

no code implementations20 Aug 2020 Aparna Elangovan, Melissa Davis, Karin Verspoor

Motivation: Protein-protein interactions (PPI) are critical to the function of proteins in both normal and diseased cells, and many critical protein functions are mediated by interactions. Knowledge of the nature of these interactions is important for the construction of networks to analyse biological data.

COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research

no code implementations18 Aug 2020 Karin Verspoor, Simon Šuster, Yulia Otmakhova, Shevon Mendis, Zenan Zhai, Biaoyan Fang, Jey Han Lau, Timothy Baldwin, Antonio Jimeno Yepes, David Martinez

We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search by providing a visual overview supporting exploration of a collection to identify key articles of interest.

WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking

1 code implementation COLING 2020 Afshin Rahimi, Timothy Baldwin, Karin Verspoor

We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources.

Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings

1 code implementation WS 2019 Zenan Zhai, Dat Quoc Nguyen, Saber A. Akhondi, Camilo Thorne, Christian Druckenbrodt, Trevor Cohn, Michelle Gregory, Karin Verspoor

In this paper, we explore the NER performance of a BiLSTM-CRF model utilising pre-trained word embeddings, character-level word representations and contextualized ELMo word representations for chemical patents.

Named Entity Recognition NER +1

End-to-end neural relation extraction using deep biaffine attention

1 code implementation29 Dec 2018 Dat Quoc Nguyen, Karin Verspoor

We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features.

General Classification Relation Classification

Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets

no code implementations WS 2018 Mariana Neves, Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Cristian Grozea, Amy Siu, Madeleine Kittner, Karin Verspoor

Machine translation enables the automatic translation of textual documents between languages and can facilitate access to information only available in a given language for non-speakers of this language, e. g. research results presented in scientific publications.

Machine Translation Translation

Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition

no code implementations WS 2018 Zenan Zhai, Dat Quoc Nguyen, Karin Verspoor

We compare the use of LSTM-based and CNN-based character-level word embeddings in BiLSTM-CRF models to approach chemical and disease named entity recognition (NER) tasks.

Named Entity Recognition NER +1

From POS tagging to dependency parsing for biomedical event extraction

2 code implementations11 Aug 2018 Dat Quoc Nguyen, Karin Verspoor

Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT.

Dependency Parsing Event Extraction +2

Adjusting for Chance Clustering Comparison Measures

no code implementations3 Dec 2015 Simone Romano, Nguyen Xuan Vinh, James Bailey, Karin Verspoor

In particular, the Adjusted Rand Index (ARI) based on pair-counting, and the Adjusted Mutual Information (AMI) based on Shannon information theory are very popular in the clustering community.

A Framework to Adjust Dependency Measure Estimates for Chance

no code implementations27 Oct 2015 Simone Romano, Nguyen Xuan Vinh, James Bailey, Karin Verspoor

For example: non-linear dependencies between two continuous variables can be explored with the Maximal Information Coefficient (MIC); and categorical variables that are dependent to the target class are selected using Gini gain in random forests.

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