Search Results for author:
Found 65 papers, 12 papers with code
GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction
During the training stage, GenerativeRE fine-tunes the pre-trained generative model and learns the special entity labels simultaneously.
Named Entity Recognition for Cancer Immunology Research Using Distant Supervision
Cancer immunology research involves several important cell and protein factors.
GenCompareSum: a hybrid unsupervised summarization method using salience
To this end, we propose a hybrid, unsupervised, abstractive-extractive approach, in which we walk through a document, generating salient textual fragments representing its key points.
Ranked #1 on
Text Summarization
on S2ORC
Learning Disentangled Representations of Negation and Uncertainty
Negation and uncertainty modeling are long-standing tasks in natural language processing.
Transformer for Graphs: An Overview from Architecture Perspective
In this survey, we provide a comprehensive review of various Graph Transformer models from the architectural design perspective.
Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, is an essential component of online advertising.
Investigating Text Simplification Evaluation
Modern text simplification (TS) heavily relies on the availability of gold standard data to build machine learning models.
One-shot to Weakly-Supervised Relation Classification using Language Models
Unfortunately, both annotation methodologies are costly and time-consuming since they depend on intensive human labour for annotation or for knowledge base creation.
EPICURE Ensemble Pretrained Models for Extracting Cancer Mutations from Literature
To interpret the genetic profile present in a patient sample, it is necessary to know which mutations have important roles in the development of the corresponding cancer type.
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors
We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs.
Ranked #4 on
Relation Extraction
on NYT Corpus
Paladin: an annotation tool based on active and proactive learning
In this paper, we present Paladin, an open-source web-based annotation tool for creating high-quality multi-label document-level datasets.
HSEarch: semantic search system for workplace accident reports
Semantic search engines, which integrate the output of text mining (TM) methods, can significantly increase the ease and efficiency of finding relevant documents and locating important information within them.
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
We propose a new model "SpanEmo" casting multi-label emotion classification as span-prediction, which can aid ER models to learn associations between labels and words in a sentence.
Ranked #1 on
Emotion Classification
on SemEval 2018 Task 1E-c
A Neural Model for Aggregating Coreference Annotation in Crowdsourcing
To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels.
DeepEventMine: end-to-end neural nested event extraction from biomedical texts
Motivation Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events.
Ranked #1 on
Event Extraction
on GENIA 2013
Semantic Annotation for Improved Safety in Construction Work
In response, we have designed a novel named entity annotation scheme and associated guidelines for this domain, which covers hazards, consequences, mitigation strategies and project attributes.
Revisiting Unsupervised Relation Extraction
Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs).
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering
Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.
A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection
We tackle the nested and overlapping event detection task and propose a novel search-based neural network (SBNN) structured prediction model that treats the task as a search problem on a relation graph of trigger-argument structures.
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
We thus propose an edge-oriented graph neural model for document-level relation extraction.
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning
The availability of large-scale and real-time data on social media has motivated research into adverse drug reactions (ADRs).
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies.
Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks
It can automatically estimate the per-instance reliability of each annotator and the correct label for each instance.
A Walk-based Model on Entity Graphs for Relation Extraction
We present a novel graph-based neural network model for relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Cross Sentence metric)
APLenty: annotation tool for creating high-quality datasets using active and proactive learning
In this paper, we present APLenty, an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning.
Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts
Existing biomedical coreference resolution systems depend on features and/or rules based on syntactic parsers.
A Neural Layered Model for Nested Named Entity Recognition
Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer.
Ranked #8 on
Named Entity Recognition
on GENIA
Paths for uncertainty: Exploring the intricacies of uncertainty identification for news
We compare the differences in the definition and expression of uncertainty between a scientific domain, i. e., biomedicine, and newswire.
Proactive Learning for Named Entity Recognition
The goal of active learning is to minimise the cost of producing an annotated dataset, in which annotators are assumed to be perfect, i. e., they always choose the correct labels.
Distributed Document and Phrase Co-embeddings for Descriptive Clustering
Descriptive document clustering aims to automatically discover groups of semantically related documents and to assign a meaningful label to characterise the content of each cluster.
Learning to recognise named entities in tweets by exploiting weakly labelled data
Named entity recognition (NER) in social media (e. g., Twitter) is a challenging task due to the noisy nature of text.
Ensemble Classification of Grants using LDA-based Features
Classifying research grants into useful categories is a vital task for a funding body to give structure to the portfolio for analysis, informing strategic planning and decision-making.
Identifying Content Types of Messages Related to Open Source Software Projects
Assessing the suitability of an Open Source Software project for adoption requires not only an analysis of aspects related to the code, such as code quality, frequency of updates and new version releases, but also an evaluation of the quality of support offered in related online forums and issue trackers.
Locating Requests among Open Source Software Communication Messages
As a first step towards assessing the quality of support offered online for Open Source Software (OSS), we address the task of locating requests, i. e., messages that raise an issue to be addressed by the OSS community, as opposed to any other message.
Interoperability and Customisation of Annotation Schemata in Argo
Argo aids the development of custom annotation schemata and supports their interoperability by featuring a schema editor and specialised analytics for schemata alignment.
The Strategic Impact of META-NET on the Regional, National and International Level
This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics.
The Meta-knowledge of Causality in Biomedical Scientific Discourse
Causality lies at the heart of biomedical knowledge, being involved in diagnosis, pathology or systems biology.
Collaborative Development and Evaluation of Text-processing Workflows in a UIMA-supported Web-based Workbench
Challenges in creating comprehensive text-processing worklows include a lack of the interoperability of individual components coming from different providers and/or a requirement imposed on the end users to know programming techniques to compose such workflows.
Biomedical Chinese-English CLIR Using an Extended CMeSH Resource to Expand Queries
All experiments compare the use of two different retrieval models, i. e. Okapi BM25 and a query likelihood language model.
A data and analysis resource for an experiment in text mining a collection of micro-blogs on a political topic.
The analysis of a corpus of micro-blogs on the topic of the 2011 UK referendum about the Alternative Vote has been undertaken as a joint activity by text miners and social scientists.
Identification of Manner in Bio-Events
Until recently, these corpora, and hence the event extraction systems trained on them, focussed almost exclusively on the identification and classification of event arguments, without taking into account how the textual context of the events could affect their interpretation.
