4,291 research outputs found
Neural Metric Learning for Fast End-to-End Relation Extraction
Relation extraction (RE) is an indispensable information extraction task in
several disciplines. RE models typically assume that named entity recognition
(NER) is already performed in a previous step by another independent model.
Several recent efforts, under the theme of end-to-end RE, seek to exploit
inter-task correlations by modeling both NER and RE tasks jointly. Earlier work
in this area commonly reduces the task to a table-filling problem wherein an
additional expensive decoding step involving beam search is applied to obtain
globally consistent cell labels. In efforts that do not employ table-filling,
global optimization in the form of CRFs with Viterbi decoding for the NER
component is still necessary for competitive performance. We introduce a novel
neural architecture utilizing the table structure, based on repeated
applications of 2D convolutions for pooling local dependency and metric-based
features, that improves on the state-of-the-art without the need for global
optimization. We validate our model on the ADE and CoNLL04 datasets for
end-to-end RE and demonstrate gain (in F-score) over prior best
results with training and testing times that are seven to ten times faster ---
the latter highly advantageous for time-sensitive end user applications
Instance Segmentation by Deep Coloring
We propose a new and, arguably, a very simple reduction of instance
segmentation to semantic segmentation. This reduction allows to train
feed-forward non-recurrent deep instance segmentation systems in an end-to-end
fashion using architectures that have been proposed for semantic segmentation.
Our approach proceeds by introducing a fixed number of labels (colors) and then
dynamically assigning object instances to those labels during training
(coloring). A standard semantic segmentation objective is then used to train a
network that can color previously unseen images. At test time, individual
object instances can be recovered from the output of the trained convolutional
network using simple connected component analysis. In the experimental
validation, the coloring approach is shown to be capable of solving diverse
instance segmentation tasks arising in autonomous driving (the Cityscapes
benchmark), plant phenotyping (the CVPPP leaf segmentation challenge), and
high-throughput microscopy image analysis.
The source code is publicly available:
https://github.com/kulikovv/DeepColoring.Comment: 10 pages, 6 figures, 3 table
MCODE: Multivariate Conditional Outlier Detection
Outlier detection aims to identify unusual data instances that deviate from
expected patterns. The outlier detection is particularly challenging when
outliers are context dependent and when they are defined by unusual
combinations of multiple outcome variable values. In this paper, we develop and
study a new conditional outlier detection approach for multivariate outcome
spaces that works by (1) transforming the conditional detection to the outlier
detection problem in a new (unconditional) space and (2) defining outlier
scores by analyzing the data in the new space. Our approach relies on the
classifier chain decomposition of the multi-dimensional classification problem
that lets us transform the output space into a probability vector, one
probability for each dimension of the output space. Outlier scores applied to
these transformed vectors are then used to detect the outliers. Experiments on
multiple multi-dimensional classification problems with the different outlier
injection rates show that our methodology is robust and able to successfully
identify outliers when outliers are either sparse (manifested in one or very
few dimensions) or dense (affecting multiple dimensions)
Overview of the gene ontology task at BioCreative IV
Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation
A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing
It is crucial to provide compatible treatment schemes for a disease according
to various symptoms at different stages. However, most classification methods
might be ineffective in accurately classifying a disease that holds the
characteristics of multiple treatment stages, various symptoms, and
multi-pathogenesis. Moreover, there are limited exchanges and cooperative
actions in disease diagnoses and treatments between different departments and
hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced
doctors might have difficulty in identifying them promptly and accurately.
Therefore, to maximize the utilization of the advanced medical technology of
developed hospitals and the rich medical knowledge of experienced doctors, a
Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in
this paper. First, to effectively identify disease symptoms more accurately, a
Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for
disease-symptom clustering. In addition, association analyses on
Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted
by the Apriori algorithm separately. The appropriate diagnosis and treatment
schemes are recommended for patients and inexperienced doctors, even if they
are in a limited therapeutic environment. Moreover, to reach the goals of high
performance and low latency response, we implement a parallel solution for
DDTRS using the Apache Spark cloud platform. Extensive experimental results
demonstrate that the proposed DDTRS realizes disease-symptom clustering
effectively and derives disease treatment recommendations intelligently and
accurately
Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey
Topic modeling is one of the most powerful techniques in text mining for data
mining, latent data discovery, and finding relationships among data, text
documents. Researchers have published many articles in the field of topic
modeling and applied in various fields such as software engineering, political
science, medical and linguistic science, etc. There are various methods for
topic modeling, which Latent Dirichlet allocation (LDA) is one of the most
popular methods in this field. Researchers have proposed various models based
on the LDA in topic modeling. According to previous work, this paper can be
very useful and valuable for introducing LDA approaches in topic modeling. In
this paper, we investigated scholarly articles highly (between 2003 to 2016)
related to Topic Modeling based on LDA to discover the research development,
current trends and intellectual structure of topic modeling. Also, we summarize
challenges and introduce famous tools and datasets in topic modeling based on
LDA.Comment: arXiv admin note: text overlap with arXiv:1505.07302 by other author
An Experience Report of Large Scale Federations
We present an experimental study of large-scale RDF federations on top of the
Bio2RDF data sources, involving 29 data sets with more than four billion RDF
triples deployed in a local federation. Our federation is driven by FedX, a
highly optimized federation mediator for Linked Data. We discuss design
decisions, technical aspects, and experiences made in setting up and optimizing
the Bio2RDF federation, and present an exhaustive experimental evaluation of
the federation scenario. In addition to a controlled setting with local
federation members, we study implications arising in a hybrid setting, where
local federation members interact with remote federation members exhibiting
higher network latency. The outcome demonstrates the feasibility of federated
semantic data management in general and indicates remaining bottlenecks and
research opportunities that shall serve as a guideline for future work in the
area of federated semantic data processing
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
Language models pretrained on text from a wide variety of sources form the
foundation of today's NLP. In light of the success of these broad-coverage
models, we investigate whether it is still helpful to tailor a pretrained model
to the domain of a target task. We present a study across four domains
(biomedical and computer science publications, news, and reviews) and eight
classification tasks, showing that a second phase of pretraining in-domain
(domain-adaptive pretraining) leads to performance gains, under both high- and
low-resource settings. Moreover, adapting to the task's unlabeled data
(task-adaptive pretraining) improves performance even after domain-adaptive
pretraining. Finally, we show that adapting to a task corpus augmented using
simple data selection strategies is an effective alternative, especially when
resources for domain-adaptive pretraining might be unavailable. Overall, we
consistently find that multi-phase adaptive pretraining offers large gains in
task performance.Comment: ACL 202
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The next generation wireless networks (i.e. 5G and beyond), which would be
extremely dynamic and complex due to the ultra-dense deployment of
heterogeneous networks (HetNets), poses many critical challenges for network
planning, operation, management and troubleshooting. At the same time,
generation and consumption of wireless data are becoming increasingly
distributed with ongoing paradigm shift from people-centric to machine-oriented
communications, making the operation of future wireless networks even more
complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with
improved context-awareness becomes extremely important. In this regard, the
emerging fog (edge) computing architecture aiming to distribute computing,
storage, control, communication, and networking functions closer to end users,
have a great potential for enabling efficient operation of future wireless
networks. These promising architectures make the adoption of artificial
intelligence (AI) principles which incorporate learning, reasoning and
decision-making mechanism, as natural choices for designing a tightly
integrated network. Towards this end, this article provides a comprehensive
survey on the utilization of AI integrating machine learning, data analytics
and natural language processing (NLP) techniques for enhancing the efficiency
of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data
acquisition, knowledge discovery, network planning, operation and management of
the next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on
communication networks and services, (To appear
Joint-ViVo: Selecting and Weighting Visual Words Jointly for Bag-of-Features based Tissue Classification in Medical Images
Automatically classifying the tissues types of Region of Interest (ROI) in
medical imaging has been an important application in Computer-Aided Diagnosis
(CAD), such as classification of breast parenchymal tissue in the mammogram,
classify lung disease patterns in High-Resolution Computed Tomography (HRCT)
etc. Recently, bag-of-features method has shown its power in this field,
treating each ROI as a set of local features. In this paper, we investigate
using the bag-of-features strategy to classify the tissue types in medical
imaging applications. Two important issues are considered here: the visual
vocabulary learning and weighting. Although there are already plenty of
algorithms to deal with them, all of them treat them independently, namely, the
vocabulary learned first and then the histogram weighted. Inspired by
Auto-Context who learns the features and classifier jointly, we try to develop
a novel algorithm that learns the vocabulary and weights jointly. The new
algorithm, called Joint-ViVo, works in an iterative way. In each iteration, we
first learn the weights for each visual word by maximizing the margin of ROI
triplets, and then select the most discriminate visual words based on the
learned weights for the next iteration. We test our algorithm on three tissue
classification tasks: identifying brain tissue type in magnetic resonance
imaging (MRI), classifying lung tissue in HRCT images, and classifying breast
tissue density in mammograms. The results show that Joint-ViVo can perform
effectively for classifying tissues.Comment: This paper has been withdrawn by the author due to the terrible
writin
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