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Found 54 papers, 14 papers with code
Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration
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.
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text
Procedural text contains rich anaphoric phenomena, yet has not received much attention in NLP.
Learning from Unlabelled Data for Clinical Semantic Textual Similarity
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.
Using Discourse Structure to Differentiate Focus Entities from Background Entities in Scientific Literature
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
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
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.
ITTC @ TREC 2021 Clinical Trials Track
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
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.
Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT
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.
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark
Researchers have explored advanced methods from computer vision and natural language processing to incorporate medical domain knowledge for the generation of readable medical reports.
Impact of detecting clinical trial elements in exploration of COVID-19 literature
The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature.
ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain
Chemical patents contain rich coreference and bridging links, which are the target of this research.
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation
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).
Memorization vs. Generalization: Quantifying Data Leakage in NLP Performance Evaluation
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).
Assigning function to protein-protein interactions: a weakly supervised BioBERT based approach using PubMed abstracts
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
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.
Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity
In this paper, we apply pre-trained language models to the Semantic Textual Similarity (STS) task, with a specific focus on the clinical domain.
Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes
Identifying the reasons for antibiotic administration in veterinary records is a critical component of understanding antimicrobial usage patterns.
WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking
We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources.
SemEval-2017 Task 3: Community Question Answering
We describe SemEval-2017 Task 3 on Community Question Answering.
Findings of the WMT 2019 Biomedical Translation Shared Task: Evaluation for MEDLINE Abstracts and Biomedical Terminologies
In the fourth edition of the WMT Biomedical Translation task, we considered a total of six languages, namely Chinese (zh), English (en), French (fr), German (de), Portuguese (pt), and Spanish (es).
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
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.
A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data
This paper focuses on a traditional relation extraction task in the context of limited annotated data and a narrow knowledge domain.
Detecting Chemical Reactions in Patents
Extracting chemical reactions from patents is a crucial task for chemists working on chemical exploration.
End-to-end neural relation extraction using deep biaffine attention
We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features.
Ranked #5 on
Relation Extraction
on CoNLL04
Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets
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.
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition
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.
From POS tagging to dependency parsing for biomedical event extraction
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.
Ranked #1 on
Dependency Parsing
on GENIA - LAS
An improved neural network model for joint POS tagging and dependency parsing
We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing.
Ranked #14 on
Dependency Parsing
on Penn Treebank
Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings
We investigate the incorporation of character-based word representations into a standard CNN-based relation extraction model.
Adjusting for Chance Clustering Comparison Measures
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
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.
