13 research outputs found
DWIE : an entity-centric dataset for multi-task document-level information extraction
This paper presents DWIE, the 'Deutsche Welle corpus for Information Extraction', a newly created multi-task dataset that combines four main Information Extraction (IE) annotation subtasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document. This contrasts with currently dominant mention-driven approaches that start from the detection and classification of named entity mentions in individual sentences. Further, DWIE presented two main challenges when building and evaluating IE models for it. First, the use of traditional mention-level evaluation metrics for NER and RE tasks on entity-centric DWIE dataset can result in measurements dominated by predictions on more frequently mentioned entities. We tackle this issue by proposing a new entity-driven metric that takes into account the number of mentions that compose each of the predicted and ground truth entities. Second, the document-level multi-task annotations require the models to transfer information between entity mentions located in different parts of the document, as well as between different tasks, in a joint learning setting. To realize this, we propose to use graph-based neural message passing techniques between document-level mention spans. Our experiments show an improvement of up to 5.5 F-1 percentage points when incorporating neural graph propagation into our joint model. This demonstrates DWIE's potential to stimulate further research in graph neural networks for representation learning in multi-task IE. We make DWIE publicly available at https://github.com/klimzaporojets/DWIE
DWIE: an entity-centric dataset for multi-task document-level information extraction
This paper presents DWIE, the 'Deutsche Welle corpus for Information
Extraction', a newly created multi-task dataset that combines four main
Information Extraction (IE) annotation subtasks: (i) Named Entity Recognition
(NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv)
Entity Linking. DWIE is conceived as an entity-centric dataset that describes
interactions and properties of conceptual entities on the level of the complete
document. This contrasts with currently dominant mention-driven approaches that
start from the detection and classification of named entity mentions in
individual sentences. Further, DWIE presented two main challenges when building
and evaluating IE models for it. First, the use of traditional mention-level
evaluation metrics for NER and RE tasks on entity-centric DWIE dataset can
result in measurements dominated by predictions on more frequently mentioned
entities. We tackle this issue by proposing a new entity-driven metric that
takes into account the number of mentions that compose each of the predicted
and ground truth entities. Second, the document-level multi-task annotations
require the models to transfer information between entity mentions located in
different parts of the document, as well as between different tasks, in a joint
learning setting. To realize this, we propose to use graph-based neural message
passing techniques between document-level mention spans. Our experiments show
an improvement of up to 5.5 F1 percentage points when incorporating neural
graph propagation into our joint model. This demonstrates DWIE's potential to
stimulate further research in graph neural networks for representation learning
in multi-task IE. We make DWIE publicly available at
https://github.com/klimzaporojets/DWIE
Predicting suicide risk from online postings in Reddit : the UGent-IDLab submission to the CLPysch 2019 Shared Task A
This paper describes IDLab’s text classification systems submitted to Task A as part of the CLPsych 2019 shared task. The aim of this shared task was to develop automated systems that predict the degree of suicide risk of people based on their posts on Reddit. Bag-of-words features, emotion features and post level predictions are used to derive user-level predictions. Linear models and ensembles of these models are used to predict final scores. We find that predicting fine-grained risk levels is much more difficult than flagging potentially at-risk users. Furthermore, we do not find clear added value from building richer ensembles compared to simple baselines, given the available training data and the nature of the prediction task
Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution
We consider a joint information extraction (IE) model, solving named entity
recognition, coreference resolution and relation extraction jointly over the
whole document. In particular, we study how to inject information from a
knowledge base (KB) in such IE model, based on unsupervised entity linking. The
used KB entity representations are learned from either (i) hyperlinked text
documents (Wikipedia), or (ii) a knowledge graph (Wikidata), and appear
complementary in raising IE performance. Representations of corresponding
entity linking (EL) candidates are added to text span representations of the
input document, and we experiment with (i) taking a weighted average of the EL
candidate representations based on their prior (in Wikipedia), and (ii) using
an attention scheme over the EL candidate list. Results demonstrate an increase
of up to 5% F1-score for the evaluated IE tasks on two datasets. Despite a
strong performance of the prior-based model, our quantitative and qualitative
analysis reveals the advantage of using the attention-based approach
Solving Math Word Problems by Scoring Equations with Recursive Neural Networks
Solving math word problems is a cornerstone task in assessing language
understanding and reasoning capabilities in NLP systems. Recent works use
automatic extraction and ranking of candidate solution equations providing the
answer to math word problems. In this work, we explore novel approaches to
score such candidate solution equations using tree-structured recursive neural
network (Tree-RNN) configurations. The advantage of this Tree-RNN approach over
using more established sequential representations, is that it can naturally
capture the structure of the equations. Our proposed method consists in
transforming the mathematical expression of the equation into an expression
tree. Further, we encode this tree into a Tree-RNN by using different Tree-LSTM
architectures. Experimental results show that our proposed method (i) improves
overall performance with more than 3% accuracy points compared to previous
state-of-the-art, and with over 18% points on a subset of problems that require
more complex reasoning, and (ii) outperforms sequential LSTMs by 4% accuracy
points on such more complex problems
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance
Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical
literature is paramount for public safety, but involves slow and costly manual
labor. We set out to improve drug safety monitoring (pharmacovigilance, PV)
through the use of Natural Language Processing (NLP). We introduce BioDEX, a
large-scale resource for Biomedical adverse Drug Event Extraction, rooted in
the historical output of drug safety reporting in the U.S. BioDEX consists of
65k abstracts and 19k full-text biomedical papers with 256k associated
document-level safety reports created by medical experts. The core features of
these reports include the reported weight, age, and biological sex of a
patient, a set of drugs taken by the patient, the drug dosages, the reactions
experienced, and whether the reaction was life threatening. In this work, we
consider the task of predicting the core information of the report given its
originating paper. We estimate human performance to be 72.0% F1, whereas our
best model achieves 62.3% F1, indicating significant headroom on this task. We
also begin to explore ways in which these models could help professional PV
reviewers. Our code and data are available: https://github.com/KarelDO/BioDEX.Comment: 28 page
Neural approaches to entity-centric information extraction
In dit proefschrift behandelen we een zeer specifiek gebied van NLP dat het begrip van entities in tekst aanpakt. We introduceren een radicaal andere, entity-centric kijk op de informatie in tekst. We stellen dat, in plaats van individuele vermeldingen in tekst te gebruiken om hun betekenis te begrijpen, we applicaties moeten bouwen die zouden werken in termen van entiteitsconcepten. Vervolgens, presenteren we een meer gedetailleerd model over hoe de entiteitsgerichte benadering kan worden gebruikt voor de taak entity linking. In ons werk laten we zien dat deze taak kan worden verbeterd door te overwegen entiteitskoppeling uit te voeren op het coreferentieclusterniveau in plaats van op elk van de vermeldingen afzonderlijk. In ons volgende werk, bestuderen we ook hoe de informatie van entiteiten uit Knowledge Base in tekst kan worden geïntegreerd. Ten slotte analyseren we de evolutie van de entiteiten vanuit een tijdsperspectief
Injecting knowledge base information into end-to-end joint entity and relation extraction and coreference resolution
We consider a joint information extraction(IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document. In particular, we study how to inject information from a knowledge base (KB) in such IE model, based on unsupervised entity linking. The used KB entity representations are learned from either(i) hyperlinked text documents (Wikipedia), or(ii) a knowledge graph (Wikidata), and ap-pear complementary in raising IE performance. Representations of corresponding entity linking (EL) candidates are added to text span representations of the input document, and we experiment with (i) taking a weighted average of the EL candidate representations based on their prior (in Wikipedia), and (ii) using an attention scheme over the EL candidate list. Results demonstrate an increase of up to 5%F1-score for the evaluated IE tasks on two datasets. Despite a strong performance of the prior-based model, our quantitative and qualitative analysis reveals the advantage of using the attention-based approach
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution
We consider the task of document-level entity linking (EL), where it is
important to make consistent decisions for entity mentions over the full
document jointly. We aim to leverage explicit "connections" among mentions
within the document itself: we propose to join the EL task with that of
coreference resolution (coref). This is complementary to related works that
exploit either (i) implicit document information (e.g., latent relations among
entity mentions, or general language models) or (ii) connections between the
candidate links (e.g, as inferred from the external knowledge base).
Specifically, we cluster mentions that are linked via coreference, and enforce
a single EL for all of the clustered mentions together. The latter constraint
has the added benefit of increased coverage by joining EL candidate lists for
the thus clustered mentions. We formulate the coref+EL problem as a structured
prediction task over directed trees and use a globally normalized model to
solve it. Experimental results on two datasets show a boost of up to +5%
F1-score on both coref and EL tasks, compared to their standalone counterparts.
For a subset of hard cases, with individual mentions lacking the correct EL in
their candidate entity list, we obtain a +50% increase in accuracy