12,130 research outputs found
TAPON: a two-phase machine learning approach for semantic labelling
Through semantic labelling we enrich structured information from sources such as HTML pages, tables, or JSON files, with labels to integrate it into a local ontology. This process involves measuring some features of the information and then nding the classes that best describe it. The problem with current techniques is that they do not model relationships between classes. Their features fall short when some classes have very similar structures or textual formats. In order to deal with this problem, we have devised TAPON: a new semantic labelling technique that computes novel features that take into account the relationships. TAPON computes these features by means of a two-phase approach. In the first phase, we compute simple features and obtain a preliminary set of labels (hints). In the second phase, we inject our novel features and obtain a refined set of labels. Our experimental results show that our technique, thanks to our rich feature catalogue and novel modelling, achieves higher accuracy than other state-of-the-art techniques.Ministerio de EconomÃa y Competitividad TIN2016-75394-
A neural network for semantic labelling of structured information
Intelligent systems rely on rich sources of information to make informed decisions. Using information from external sources requires establishing correspondences between the information and known information classes. This can be achieved with semantic labelling, which assigns known labels to structured information by classifying it according to computed features. The existing proposals have explored different sets of features, without focusing on what classification techniques are used. In this paper we present three contributions: first, insights on architectural issues that arise when using neural networks for semantic labelling; second, a novel implementation of semantic labelling that uses a state-of-the-art neural network classifier which achieves significantly better results than other four traditional classifiers; third, a comparison of the results obtained by the former network when using different subsets of features, comparing textual features to structural ones, and domain-dependent features to domain-independent ones. The experiments were carried away with datasets from three real world sources. Our results show that there is a need to develop more semantic labelling proposals with sophisticated classification techniques and large features catalogues.Ministerio de EconomÃa y Competitividad TIN2016-75394-
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
In this paper, we propose an alternative method to estimate room layouts of
cluttered indoor scenes. This method enjoys the benefits of two novel
techniques. The first one is semantic transfer (ST), which is: (1) a
formulation to integrate the relationship between scene clutter and room layout
into convolutional neural networks; (2) an architecture that can be end-to-end
trained; (3) a practical strategy to initialize weights for very deep networks
under unbalanced training data distribution. ST allows us to extract highly
robust features under various circumstances, and in order to address the
computation redundance hidden in these features we develop a principled and
efficient inference scheme named physics inspired optimization (PIO). PIO's
basic idea is to formulate some phenomena observed in ST features into
mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the
proposed method is more accurate than state-of-the-art methods.Comment: To appear in CVPR 2017. Project Page:
https://sites.google.com/view/st-pio
Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
Adverse drug reactions (ADRs) are unwanted or harmful effects experienced
after the administration of a certain drug or a combination of drugs,
presenting a challenge for drug development and drug administration. In this
paper, we present a set of taggers for extracting adverse drug reactions and
related entities, including factors, severity, negations, drug class and
animal. The systems used a mix of rule-based, machine learning (CRF) and deep
learning (BLSTM with word2vec embeddings) methodologies in order to annotate
the data. The systems were submitted to adverse drug reaction shared task,
organised during Text Analytics Conference in 2017 by National Institute for
Standards and Technology, archiving F1-scores of 76.00 and 75.61 respectively.Comment: Paper describing submission for TAC ADR shared tas
Probabilistic Argumentation. An Equational Approach
There is a generic way to add any new feature to a system. It involves 1)
identifying the basic units which build up the system and 2) introducing the
new feature to each of these basic units.
In the case where the system is argumentation and the feature is
probabilistic we have the following. The basic units are: a. the nature of the
arguments involved; b. the membership relation in the set S of arguments; c.
the attack relation; and d. the choice of extensions.
Generically to add a new aspect (probabilistic, or fuzzy, or temporal, etc)
to an argumentation network can be done by adding this feature to each
component a-d. This is a brute-force method and may yield a non-intuitive or
meaningful result.
A better way is to meaningfully translate the object system into another
target system which does have the aspect required and then let the target
system endow the aspect on the initial system. In our case we translate
argumentation into classical propositional logic and get probabilistic
argumentation from the translation.
Of course what we get depends on how we translate.
In fact, in this paper we introduce probabilistic semantics to abstract
argumentation theory based on the equational approach to argumentation
networks. We then compare our semantics with existing proposals in the
literature including the approaches by M. Thimm and by A. Hunter. Our
methodology in general is discussed in the conclusion
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