213,211 research outputs found
Temporal Common Sense Acquisition with Minimal Supervision
Temporal common sense (e.g., duration and frequency of events) is crucial for
understanding natural language. However, its acquisition is challenging, partly
because such information is often not expressed explicitly in text, and human
annotation on such concepts is costly. This work proposes a novel sequence
modeling approach that exploits explicit and implicit mentions of temporal
common sense, extracted from a large corpus, to build TACOLM, a temporal common
sense language model. Our method is shown to give quality predictions of
various dimensions of temporal common sense (on UDST and a newly collected
dataset from RealNews). It also produces representations of events for relevant
tasks such as duration comparison, parent-child relations, event coreference
and temporal QA (on TimeBank, HiEVE and MCTACO) that are better than using the
standard BERT. Thus, it will be an important component of temporal NLP.Comment: Accepted by ACL 202
Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings
Causality extraction from natural language texts is a challenging open
problem in artificial intelligence. Existing methods utilize patterns,
constraints, and machine learning techniques to extract causality, heavily
depending on domain knowledge and requiring considerable human effort and time
for feature engineering. In this paper, we formulate causality extraction as a
sequence labeling problem based on a novel causality tagging scheme. On this
basis, we propose a neural causality extractor with the BiLSTM-CRF model as the
backbone, named SCITE (Self-attentive BiLSTM-CRF wIth Transferred Embeddings),
which can directly extract cause and effect without extracting candidate causal
pairs and identifying their relations separately. To address the problem of
data insufficiency, we transfer contextual string embeddings, also known as
Flair embeddings, which are trained on a large corpus in our task. In addition,
to improve the performance of causality extraction, we introduce a multihead
self-attention mechanism into SCITE to learn the dependencies between causal
words. We evaluate our method on a public dataset, and experimental results
demonstrate that our method achieves significant and consistent improvement
compared to baselines.Comment: 39 pages, 11 figures, 6 table
Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts
We present an analysis of the problem of identifying biological context and
associating it with biochemical events in biomedical texts. This constitutes a
non-trivial, inter-sentential relation extraction task. We focus on biological
context as descriptions of the species, tissue type and cell type that are
associated with biochemical events. We describe the properties of an annotated
corpus of context-event relations and present and evaluate several classifiers
for context-event association trained on syntactic, distance and frequency
features
Summarizing Reports on Evolving Events; Part I: Linear Evolution
We present an approach for summarization from multiple documents which report
on events that evolve through time, taking into account the different document
sources. We distinguish the evolution of an event into linear and non-linear.
According to our approach, each document is represented by a collection of
messages which are then used in order to instantiate the cross-document
relations that determine the summary content. The paper presents the
summarization system that implements this approach through a case study on
linear evolution.Comment: 7 pages. Published on the Conference Recent Advances in Natural
Language Processing (RANLP, 2005
Word-Level Loss Extensions for Neural Temporal Relation Classification
Unsupervised pre-trained word embeddings are used effectively for many tasks
in natural language processing to leverage unlabeled textual data. Often these
embeddings are either used as initializations or as fixed word representations
for task-specific classification models. In this work, we extend our
classification model's task loss with an unsupervised auxiliary loss on the
word-embedding level of the model. This is to ensure that the learned word
representations contain both task-specific features, learned from the
supervised loss component, and more general features learned from the
unsupervised loss component. We evaluate our approach on the task of temporal
relation extraction, in particular, narrative containment relation extraction
from clinical records, and show that continued training of the embeddings on
the unsupervised objective together with the task objective gives better
task-specific embeddings, and results in an improvement over the state of the
art on the THYME dataset, using only a general-domain part-of-speech tagger as
linguistic resource.Comment: Accepted at the 27th International Conference on Computational
Linguistics (COLING 2018
Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Past work in relation extraction has focused on binary relations in single
sentences. Recent NLP inroads in high-value domains have sparked interest in
the more general setting of extracting n-ary relations that span multiple
sentences. In this paper, we explore a general relation extraction framework
based on graph long short-term memory networks (graph LSTMs) that can be easily
extended to cross-sentence n-ary relation extraction. The graph formulation
provides a unified way of exploring different LSTM approaches and incorporating
various intra-sentential and inter-sentential dependencies, such as sequential,
syntactic, and discourse relations. A robust contextual representation is
learned for the entities, which serves as input to the relation classifier.
This simplifies handling of relations with arbitrary arity, and enables
multi-task learning with related relations. We evaluate this framework in two
important precision medicine settings, demonstrating its effectiveness with
both conventional supervised learning and distant supervision. Cross-sentence
extraction produced larger knowledge bases. and multi-task learning
significantly improved extraction accuracy. A thorough analysis of various LSTM
approaches yielded useful insight the impact of linguistic analysis on
extraction accuracy.Comment: Conditional accepted by TACL in December 2016; published in April
2017; presented at ACL in August 201
Neural Ranking Models for Temporal Dependency Structure Parsing
We design and build the first neural temporal dependency parser. It utilizes
a neural ranking model with minimal feature engineering, and parses time
expressions and events in a text into a temporal dependency tree structure. We
evaluate our parser on two domains: news reports and narrative stories. In a
parsing-only evaluation setup where gold time expressions and events are
provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled
parsing respectively, a result that is very competitive against alternative
approaches. In an end-to-end evaluation setup where time expressions and events
are automatically recognized, our parser beats two strong baselines on both
data domains. Our experimental results and discussions shed light on the nature
of temporal dependency structures in different domains and provide insights
that we believe will be valuable to future research in this area.Comment: 11 pages, 2 figures, 7 tables, to appear at EMNLP 2018, Proceedings
of the 2018 Conference on Empirical Methods in Natural Language Processing
(EMNLP). 201
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
Extracting temporal relations (before, after, overlapping, etc.) is a key
aspect of understanding events described in natural language. We argue that
this task would gain from the availability of a resource that provides prior
knowledge in the form of the temporal order that events usually follow. This
paper develops such a resource -- a probabilistic knowledge base acquired in
the news domain -- by extracting temporal relations between events from the New
York Times (NYT) articles over a 20-year span (1987--2007). We show that
existing temporal extraction systems can be improved via this resource. As a
byproduct, we also show that interesting statistics can be retrieved from this
resource, which can potentially benefit other time-aware tasks. The proposed
system and resource are both publicly available.Comment: 13 pages, 3 figures, accepted by NAACL'1
Geolocating Political Events in Text
This work introduces a general method for automatically finding the locations
where political events in text occurred. Using a novel set of 8,000 labeled
sentences, I create a method to link automatically extracted events and
locations in text. The model achieves human level performance on the annotation
task and outperforms previous event geolocation systems. It can be applied to
most event extraction systems across geographic contexts. I formalize the
event--location linking task, describe the neural network model, describe the
potential uses of such a system in political science, and demonstrate a
workflow to answer an open question on the role of conventional military
offensives in causing civilian casualties in the Syrian civil war.Comment: NAACL 2019, NLP+CSS Worksho
A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution
We present a novel hierarchical distance-dependent Bayesian model for event
coreference resolution. While existing generative models for event coreference
resolution are completely unsupervised, our model allows for the incorporation
of pairwise distances between event mentions -- information that is widely used
in supervised coreference models to guide the generative clustering processing
for better event clustering both within and across documents. We model the
distances between event mentions using a feature-rich learnable distance
function and encode them as Bayesian priors for nonparametric clustering.
Experiments on the ECB+ corpus show that our model outperforms state-of-the-art
methods for both within- and cross-document event coreference resolution.Comment: 12 pages, 3 figure
- …