9,893 research outputs found
Can a Machine Replace Humans in Building Regular Expressions? A Case Study
Regular expressions are routinely used in a variety of different application domains. But building a regular expression involves a considerable amount of skill, expertise, and creativity. In this work, the authors investigate whether a machine can surrogate these qualities and automatically construct regular expressions for tasks of realistic complexity. They discuss a large-scale experiment involving more than 1,700 users on 10 challenging tasks. The authors compare the solutions constructed by these users to those constructed by a tool based on genetic programming that they recently developed and made publicly available. The quality of automatically constructed solutions turned out to be similar to the quality of those constructed by the most skilled user group; the time for automatic construction was likewise similar to the time required by human users
Semantically-Enhanced Information Extraction
Information Extraction using Natural Language Processing (NLP) produces entities along with some of the relationships that may exist among them. To be semantically useful, however, such discrete extractions must be put into context through some form of intelligent analysis. This paper1,2 offers a two-part architecture that employs the statistical methods of traditional NLP to extract discrete information elements in a relatively domain-agnostic manner, which are then injected into an inference-enabled environment where they can be semantically analyzed. Within this semantic environment, extractions are woven into the contextual fabric of a user-provided, domain-centric ontology where users together with user-provided logic can analyze these extractions within a more contextually complete picture. Our demonstration system infers the possibility of a terrorist plot by extracting key events and relationships from a collection of news articles and intelligence reports
PadChest: A large chest x-ray image dataset with multi-label annotated reports
We present a labeled large-scale, high resolution chest x-ray dataset for the
automated exploration of medical images along with their associated reports.
This dataset includes more than 160,000 images obtained from 67,000 patients
that were interpreted and reported by radiologists at Hospital San Juan
Hospital (Spain) from 2009 to 2017, covering six different position views and
additional information on image acquisition and patient demography. The reports
were labeled with 174 different radiographic findings, 19 differential
diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and
mapped onto standard Unified Medical Language System (UMLS) terminology. Of
these reports, 27% were manually annotated by trained physicians and the
remaining set was labeled using a supervised method based on a recurrent neural
network with attention mechanisms. The labels generated were then validated in
an independent test set achieving a 0.93 Micro-F1 score. To the best of our
knowledge, this is one of the largest public chest x-ray database suitable for
training supervised models concerning radiographs, and the first to contain
radiographic reports in Spanish. The PadChest dataset can be downloaded from
http://bimcv.cipf.es/bimcv-projects/padchest/
Joint Extraction of Uyghur Medicine Knowledge with Edge Computing
Medical knowledge extraction methods based on edge computing deploy deep
learning models on edge devices to achieve localized entity and relation
extraction. This approach avoids transferring substantial sensitive data to
cloud data centers, effectively safeguarding the privacy of healthcare
services. However, existing relation extraction methods mainly employ a
sequential pipeline approach, which classifies relations between determined
entities after entity recognition. This mode faces challenges such as error
propagation between tasks, insufficient consideration of dependencies between
the two subtasks, and the neglect of interrelations between different relations
within a sentence. To address these challenges, a joint extraction model with
parameter sharing in edge computing is proposed, named CoEx-Bert. This model
leverages shared parameterization between two models to jointly extract
entities and relations. Specifically, CoEx-Bert employs two models, each
separately sharing hidden layer parameters, and combines these two loss
functions for joint backpropagation to optimize the model parameters.
Additionally, it effectively resolves the issue of entity overlapping when
extracting knowledge from unstructured Uyghur medical texts by considering
contextual relations. Finally, this model is deployed on edge devices for
real-time extraction and inference of Uyghur medical knowledge. Experimental
results demonstrate that CoEx-Bert outperforms existing state-of-the-art
methods, achieving accuracy, recall, and F1 scores of 90.65\%, 92.45\%, and
91.54\%, respectively, in the Uyghur traditional medical literature dataset.
These improvements represent a 6.45\% increase in accuracy, a 9.45\% increase
in recall, and a 7.95\% increase in F1 score compared to the baseline.Comment: 11 pages,6 figures,Has been accepted by Tsinghua Science and
Technolog
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
We present a large-scale collection of diverse natural language inference
(NLI) datasets that help provide insight into how well a sentence
representation captures distinct types of reasoning. The collection results
from recasting 13 existing datasets from 7 semantic phenomena into a common NLI
structure, resulting in over half a million labeled context-hypothesis pairs in
total. We refer to our collection as the DNC: Diverse Natural Language
Inference Collection. The DNC is available online at https://www.decomp.net,
and will grow over time as additional resources are recast and added from novel
sources.Comment: To be presented at EMNLP 2018. 15 page
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Learning Text Patterns using Separate-and-Conquer Genetic Programming
The problem of extracting knowledge from large volumes of unstructured textual information has become increasingly important. We consider the problem of extracting text slices that adhere to a syntactic pattern and propose an approach capable of generating the desired pattern automatically, from a few annotated examples. Our approach is based on Genetic Programming and generates extraction patterns in the form of regular expressions that may be input to existing engines without any post-processing. Key feature of our proposal is its ability of discovering automatically whether the extraction task may be solved by a single pattern, or rather a set of multiple patterns is required. We obtain this property by means of a separate-and-conquer strategy: once a candidate pattern provides adequate performance on a subset of the examples, the pattern is inserted into the set of final solutions and the evolutionary search continues on a smaller set of examples including only those not yet solved adequately. Our proposal outperforms an earlier state-of-the-art approach on three challenging datasets
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