2,278 research outputs found
Automatic Extraction of Commonsense LocatedNear Knowledge
LocatedNear relation is a kind of commonsense knowledge describing two
physical objects that are typically found near each other in real life. In this
paper, we study how to automatically extract such relationship through a
sentence-level relation classifier and aggregating the scores of entity pairs
from a large corpus. Also, we release two benchmark datasets for evaluation and
future research.Comment: Accepted by ACL 2018. A preliminary version is presented on
AKBC@NIPS'1
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
There has been a steady need in the medical community to precisely extract
the temporal relations between clinical events. In particular, temporal
information can facilitate a variety of downstream applications such as case
report retrieval and medical question answering. Existing methods either
require expensive feature engineering or are incapable of modeling the global
relational dependencies among the events. In this paper, we propose a novel
method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic
Regularization and Global Inference (CTRL-PG) to tackle the problem at the
document level. Extensive experiments on two benchmark datasets, I2B2-2012 and
TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods
for temporal relation extraction.Comment: 10 pages, 4 figures, 7 tables, accepted by AAAI 202
Syntactic Fusion: Enhancing Aspect-Level Sentiment Analysis Through Multi-Tree Graph Integration
Recent progress in aspect-level sentiment classification has been propelled
by the incorporation of graph neural networks (GNNs) leveraging syntactic
structures, particularly dependency trees. Nevertheless, the performance of
these models is often hampered by the innate inaccuracies of parsing
algorithms. To mitigate this challenge, we introduce SynthFusion, an innovative
graph ensemble method that amalgamates predictions from multiple parsers. This
strategy blends diverse dependency relations prior to the application of GNNs,
enhancing robustness against parsing errors while avoiding extra computational
burdens. SynthFusion circumvents the pitfalls of overparameterization and
diminishes the risk of overfitting, prevalent in models with stacked GNN
layers, by optimizing graph connectivity. Our empirical evaluations on the
SemEval14 and Twitter14 datasets affirm that SynthFusion not only outshines
models reliant on single dependency trees but also eclipses alternative
ensemble techniques, achieving this without an escalation in model complexity
Training Complex Models with Multi-Task Weak Supervision
As machine learning models continue to increase in complexity, collecting
large hand-labeled training sets has become one of the biggest roadblocks in
practice. Instead, weaker forms of supervision that provide noisier but cheaper
labels are often used. However, these weak supervision sources have diverse and
unknown accuracies, may output correlated labels, and may label different tasks
or apply at different levels of granularity. We propose a framework for
integrating and modeling such weak supervision sources by viewing them as
labeling different related sub-tasks of a problem, which we refer to as the
multi-task weak supervision setting. We show that by solving a matrix
completion-style problem, we can recover the accuracies of these multi-task
sources given their dependency structure, but without any labeled data, leading
to higher-quality supervision for training an end model. Theoretically, we show
that the generalization error of models trained with this approach improves
with the number of unlabeled data points, and characterize the scaling with
respect to the task and dependency structures. On three fine-grained
classification problems, we show that our approach leads to average gains of
20.2 points in accuracy over a traditional supervised approach, 6.8 points over
a majority vote baseline, and 4.1 points over a previously proposed weak
supervision method that models tasks separately
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