677 research outputs found
Semi-supervised SRL system with Bayesian inference
International audienceWe propose a new approach to perform semi-supervised training of Semantic Role Labeling models with very few amount of initial labeled data. The proposed approach combines in a novel way supervised and unsupervised training, by forcing the supervised classifier to over-generate potential semantic candidates, and then letting unsupervised inference choose the best ones. Hence, the supervised classifier can be trained on a very small corpus and with coarse-grain features, because its precision does not need to be high: its role is mainly to constrain Bayesian inference to explore only a limited part of the full search space. This approach is evaluated on French and English. In both cases, it achieves very good performance and outperforms a strong supervised baseline when only a small number of annotated sentences is available and even without using any previously trained syntactic parser
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Minimal supervision for language learning: bootstrapping global patterns from local knowledge
A fundamental step in sentence comprehension involves assigning semantic roles
to sentence constituents. To accomplish this, the listener
must parse the sentence, find constituents that are candidate arguments, and
assign semantic roles to those constituents. Each step depends on prior lexical
and syntactic knowledge. Where do children begin in solving this problem when
learning their first languages? To experiment with different representations
that children may use to begin understanding language, we have built a
computational model for this early point in language acquisition. This system,
BabySRL, learns from transcriptions of natural child-directed speech and makes
use of psycholinguistically plausible background knowledge and realistically
noisy semantic feedback to begin to classify sentences at the level of ``who
does what to whom.''
Starting with simple, psycholinguistically-motivated representations of
sentence structure, the BabySRL is able to learn from full semantic feedback,
as well as a supervision signal derived from partial semantic background
knowledge. In addition we combine the BabySRL with an unsupervised Hidden
Markov Model part-of-speech tagger, linking clusters with syntactic categories
using background noun knowledge so that they can be used to parse input for the
SRL system. The results show that proposed shallow representations of sentence
structure are robust to reductions in parsing accuracy, and that the
contribution of alternative representations of sentence structure to successful
semantic role labeling varies with the integrity of the parsing and
argument-identification stages. Finally, we enable the BabySRL to improve both
an intermediate syntactic representation and its final semantic role
classification. Using this system we show that it is possible for a simple
learner in a plausible (noisy) setup to begin comprehending simple semantics
when initialized with a small amount of concrete noun knowledge and some simple
syntax-semantics mapping biases, before acquiring any specific verb knowledge
05051 Abstracts Collection -- Probabilistic, Logical and Relational Learning - Towards a Synthesis
From 30.01.05 to 04.02.05, the Dagstuhl Seminar 05051 ``Probabilistic, Logical and Relational Learning - Towards a Synthesis\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Emergent limits of an indirect measurement from phase transitions of inference
Measurements are inseparable from inference, where the estimation of signals
of interest from other observations is called an indirect measurement. While a
variety of measurement limits have been defined by the physical constraint on
each setup, the fundamental limit of an indirect measurement is essentially the
limit of inference. Here, we propose the concept of statistical limits on
indirect measurement: the bounds of distinction between signals and noise and
between a signal and another signal. By developing the asymptotic theory of
Bayesian regression, we investigate the phenomenology of a typical indirect
measurement and demonstrate the existence of these limits. Based on the
connection between inference and statistical physics, we also provide a unified
interpretation in which these limits emerge from phase transitions of
inference. Our results could pave the way for novel experimental design,
enabling assess to the required quality of observations according to the
assumed ground truth before the concerned indirect measurement is actually
performed
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Learning to Extract Action Descriptions from Narrative Text
This paper focuses on the mapping of natural language sentences in written stories to a structured knowledge representation. This process yields an exponential explosion of instance combinations since each sentence may contain a set of ambiguous terms, each one giving place to a set of instance candidates. The selection of the best combination of instances is a structured classification problem that yields a highdemanding combinatorial optimization problem which, in this paper, is approached by a novel and efficient formulation of a genetic algorithm, which is able to exploit the conditional independence among variables, while improving the parallel scalability. The automatic rating of the resulting set of instance combinations, i.e. possible text interpretations, demands an exhaustive exploitation of the state-of-the-art resources in natural language processing to feed the system with pieces of evidence to be fused by the proposed framework. In this sense, a mapping framework able to reason with uncertainty, to integrate supervision, and evidence from external sources, was adopted. To improve the generalization capacity while learning from a limited amount of annotated data, a new constrained learning algorithm for Bayesian networks is introduced. This algorithm bounds the search space through a set of constraints which encode information on mutually exclusive values. The mapping of natural language utterances to a structured knowledge representation is important in the context of game construction, e.g. in an RPG setting, as it alleviates the manual knowledge acquisition bottleneck. The effectiveness of the proposed algorithm is evaluated on a set of three stories, yielding nine experiments. Our mapping framework yields performance gains in predicting the most likely structured representations of sentences when compared with a baseline algorithm
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