44 research outputs found
Crowdsourcing Semantic Label Propagation in Relation Classification
Distant supervision is a popular method for performing relation extraction
from text that is known to produce noisy labels. Most progress in relation
extraction and classification has been made with crowdsourced corrections to
distant-supervised labels, and there is evidence that indicates still more
would be better. In this paper, we explore the problem of propagating human
annotation signals gathered for open-domain relation classification through the
CrowdTruth methodology for crowdsourcing, that captures ambiguity in
annotations by measuring inter-annotator disagreement. Our approach propagates
annotations to sentences that are similar in a low dimensional embedding space,
expanding the number of labels by two orders of magnitude. Our experiments show
significant improvement in a sentence-level multi-class relation classifier.Comment: In publication at the First Workshop on Fact Extraction and
Verification (FeVer) at EMNLP 201
CrowdTruth 2.0: Quality Metrics for Crowdsourcing with Disagreement
Typically crowdsourcing-based approaches to gather annotated data use
inter-annotator agreement as a measure of quality. However, in many domains,
there is ambiguity in the data, as well as a multitude of perspectives of the
information examples. In this paper, we present ongoing work into the
CrowdTruth metrics, that capture and interpret inter-annotator disagreement in
crowdsourcing. The CrowdTruth metrics model the inter-dependency between the
three main components of a crowdsourcing system -- worker, input data, and
annotation. The goal of the metrics is to capture the degree of ambiguity in
each of these three components. The metrics are available online at
https://github.com/CrowdTruth/CrowdTruth-core
A Crowdsourced Frame Disambiguation Corpus with Ambiguity
We present a resource for the task of FrameNet semantic frame disambiguation
of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations
were collected using a novel crowdsourcing approach with multiple workers per
sentence to capture inter-annotator disagreement. In contrast to the typical
approach of attributing the best single frame to each word, we provide a list
of frames with disagreement-based scores that express the confidence with which
each frame applies to the word. This is based on the idea that inter-annotator
disagreement is at least partly caused by ambiguity that is inherent to the
text and frames. We have found many examples where the semantics of individual
frames overlap sufficiently to make them acceptable alternatives for
interpreting a sentence. We have argued that ignoring this ambiguity creates an
overly arbitrary target for training and evaluating natural language processing
systems - if humans cannot agree, why would we expect the correct answer from a
machine to be any different? To process this data we also utilized an expanded
lemma-set provided by the Framester system, which merges FN with WordNet to
enhance coverage. Our dataset includes annotations of 1,000 sentence-word pairs
whose lemmas are not part of FN. Finally we present metrics for evaluating
frame disambiguation systems that account for ambiguity.Comment: Accepted to NAACL-HLT201
Empirical Methodology for Crowdsourcing Ground Truth
The process of gathering ground truth data through human annotation is a
major bottleneck in the use of information extraction methods for populating
the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the
attempt to solve the issues related to volume of data and lack of annotators.
Typically these practices use inter-annotator agreement as a measure of
quality. However, in many domains, such as event detection, there is ambiguity
in the data, as well as a multitude of perspectives of the information
examples. We present an empirically derived methodology for efficiently
gathering of ground truth data in a diverse set of use cases covering a variety
of domains and annotation tasks. Central to our approach is the use of
CrowdTruth metrics that capture inter-annotator disagreement. We show that
measuring disagreement is essential for acquiring a high quality ground truth.
We achieve this by comparing the quality of the data aggregated with CrowdTruth
metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical
Relation Extraction, Twitter Event Identification, News Event Extraction and
Sound Interpretation. We also show that an increased number of crowd workers
leads to growth and stabilization in the quality of annotations, going against
the usual practice of employing a small number of annotators.Comment: in publication at the Semantic Web Journa
Capturing Ambiguity in Crowdsourcing Frame Disambiguation
FrameNet is a computational linguistics resource composed of semantic frames,
high-level concepts that represent the meanings of words. In this paper, we
present an approach to gather frame disambiguation annotations in sentences
using a crowdsourcing approach with multiple workers per sentence to capture
inter-annotator disagreement. We perform an experiment over a set of 433
sentences annotated with frames from the FrameNet corpus, and show that the
aggregated crowd annotations achieve an F1 score greater than 0.67 as compared
to expert linguists. We highlight cases where the crowd annotation was correct
even though the expert is in disagreement, arguing for the need to have
multiple annotators per sentence. Most importantly, we examine cases in which
crowd workers could not agree, and demonstrate that these cases exhibit
ambiguity, either in the sentence, frame, or the task itself, and argue that
collapsing such cases to a single, discrete truth value (i.e. correct or
incorrect) is inappropriate, creating arbitrary targets for machine learning.Comment: in publication at the sixth AAAI Conference on Human Computation and
Crowdsourcing (HCOMP) 201
Computational Controversy
Climate change, vaccination, abortion, Trump: Many topics are surrounded by
fierce controversies. The nature of such heated debates and their elements have
been studied extensively in the social science literature. More recently,
various computational approaches to controversy analysis have appeared, using
new data sources such as Wikipedia, which help us now better understand these
phenomena. However, compared to what social sciences have discovered about such
debates, the existing computational approaches mostly focus on just a few of
the many important aspects around the concept of controversies. In order to
link the two strands, we provide and evaluate here a controversy model that is
both, rooted in the findings of the social science literature and at the same
time strongly linked to computational methods. We show how this model can lead
to computational controversy analytics that have full coverage over all the
crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social
Informatics (SocInfo) 201