33,689 research outputs found
Fully Automated Fact Checking Using External Sources
Given the constantly growing proliferation of false claims online in recent
years, there has been also a growing research interest in automatically
distinguishing false rumors from factually true claims. Here, we propose a
general-purpose framework for fully-automatic fact checking using external
sources, tapping the potential of the entire Web as a knowledge source to
confirm or reject a claim. Our framework uses a deep neural network with LSTM
text encoding to combine semantic kernels with task-specific embeddings that
encode a claim together with pieces of potentially-relevant text fragments from
the Web, taking the source reliability into account. The evaluation results
show good performance on two different tasks and datasets: (i) rumor detection
and (ii) fact checking of the answers to a question in community question
answering forums.Comment: RANLP-201
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Semantic memory redux: an experimental test of hierarchical category representation
Four experiments investigated the classic issue in semantic memory of whether people organize categorical information in hierarchies and use inference to retrieve information from them, as proposed by Collins & Quillian (1969). Past evidence has focused on RT to confirm sentences such as “All birds are animals” or “Canaries breathe.” However, confounding variables such as familiarity and associations between the terms have led to contradictory results. Our experiments avoided such problems by teaching subjects novel materials. Experiment 1 tested an implicit hierarchical structure in the features of a set of studied objects (e.g., all brown objects were large). Experiment 2 taught subjects nested categories of artificial bugs. In Experiment 3, subjects learned a tree structure of novel category hierarchies. In all three, the results differed from the predictions of the hierarchical inference model. In Experiment 4, subjects learned a hierarchy by means of paired associates of novel category names. Here we finally found the RT signature of hierarchical inference. We conclude that it is possible to store information in a hierarchy and retrieve it via inference, but it is difficult and avoided whenever possible. The results are more consistent with feature comparison models than hierarchical models of semantic memory
Stochastic Answer Networks for Machine Reading Comprehension
We propose a simple yet robust stochastic answer network (SAN) that simulates
multi-step reasoning in machine reading comprehension. Compared to previous
work such as ReasoNet which used reinforcement learning to determine the number
of steps, the unique feature is the use of a kind of stochastic prediction
dropout on the answer module (final layer) of the neural network during the
training. We show that this simple trick improves robustness and achieves
results competitive to the state-of-the-art on the Stanford Question Answering
Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading
COmprehension Dataset (MS MARCO).Comment: 11 pages, 5 figures, Accepted to ACL 201
Defect prediction with bad smells in code
Background: Defect prediction in software can be highly beneficial for
development projects, when prediction is highly effective and defect-prone
areas are predicted correctly. One of the key elements to gain effective
software defect prediction is proper selection of metrics used for dataset
preparation. Objective: The purpose of this research is to verify, whether code
smells metrics, collected using Microsoft CodeAnalysis tool, added to basic
metric set, can improve defect prediction in industrial software development
project. Results: We verified, if dataset extension by the code smells sourced
metrics, change the effectiveness of the defect prediction by comparing
prediction results for datasets with and without code smells-oriented metrics.
In a result, we observed only small improvement of effectiveness of defect
prediction when dataset extended with bad smells metrics was used: average
accuracy value increased by 0.0091 and stayed within the margin of error.
However, when only use of code smells based metrics were used for prediction
(without basic set of metrics), such process resulted with surprisingly high
accuracy (0.8249) and F-measure (0.8286) results. We also elaborated data
anomalies and problems we observed when two different metric sources were used
to prepare one, consistent set of data. Conclusion: Extending the dataset by
the code smells sourced metric does not significantly improve the prediction
effectiveness. Achieved result did not compensate effort needed to collect
additional metrics. However, we observed that defect prediction based on the
code smells only is still highly effective and can be used especially where
other metrics hardly be used.Comment: Chapter 10 in Software Engineering: Improving Practice through
Research (B. Hnatkowska and M. \'Smia{\l}ek, eds.), pp. 163-176, 201
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
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