1,721,036 research outputs found
Efficient graph Kernels for textual entailment recognition
No full textOne of the most important research area in Natural Language Processing concerns the
modeling of semantics expressed in text. Since foundational work in Natural Language Understanding
has shown that a deep semantic approach is still not feasible, current research is focused on
shallow methods combining linguistic models and machine learning techniques. The latter aim at
learning semantic models, like those that can detect the entailment between the meaning of two text
fragments, by means of training examples described by specific features. These are rather difficult
to design since there is no linguistic model that can effectively encode the lexico-syntactic level of a
sentence and its corresponding semantic models. Thus, the adopted solution consists in exhaustively
describing training examples by means of all possible combinations of sentence words and syntactic
information. The latter, typically expressed as parse trees of text fragments, is often encoded in the
learning process using graph algorithms.
In this paper, we propose a class of graphs, the tripartite directed acyclic graphs (tDAGs), which
can be efficiently used to design algorithms for graph kernels for semantic natural language tasks
involving sentence pairs. These model the matching between two pairs of syntactic trees in terms of
all possible graph fragments. Interestingly, since tDAGs encode the association between identical or
similar words (i.e. variables), it can be used to represent and learn first-order rules, i.e. rules describable
by first-order logic. We prove that our matching function is a valid kernel and we empirically
show that, although its evaluation is still exponential in the worst case, it is extremely efficient and
more accurate than the previously proposed kernels
Fast On-line Kernel Learning for Trees
No full textKernel methods have been shown to be very effective for applications requiring the modeling of structured objects. However kernels for structures usually are too computational demanding to be applied to complex learning algorithms, e.g. Support Vector Machines. Consequently, in order to apply kernels to large amount of structured data, we need fast on-line algorithms along with an efficiency optimization of kernel-based computations. In this paper, we optimize this computation by representing set of trees by minimal Direct Acyclic Graphs (DAGs) allowing us i) to reduce the storage requirements and ii) to speed up the evaluation on large number of trees as it can be done 'one-shot' by computing kernels over DAGs. The experiments on predicate argument subtrees from PropBank data show that substantial computational savings can be obtained for the perceptron algorithm
KeLP at SemEval-2016 task 3: Learning semantic relations between questions and answers
No full textThis paper describes the KeLP system participating in the SemEval-2016 Community Question Answering (cQA) task. The challenge tasks are modeled as binary classification problems: kernel-based classifiers are trained on the SemEval datasets and their scores are used to sort the instances and produce the final ranking. All classifiers and kernels have been implemented within the Kernel-based Learning Platform called KeLP. Our primary submission ranked first in Subtask A, third in Subtask B and second in Subtask C. These ranks are based on MAP, which is the referring challenge system score. Our approach outperforms all the other systems with respect to all the other challenge metrics
A Machine learning approach to textual entailment recognition
Get PDFDesigning models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so
Syntactic/semantic structures for textual entailment recognition
Full text linkIn this paper, we describe an approach based
on off-the-shelf parsers and semantic resources
for the Recognizing Textual Entailment
(RTE) challenge that can be generally
applied to any domain. Syntax is exploited
by means of tree kernels whereas lexical semantics
is derived from heterogeneous resources,
e.g. WordNet or distributional semantics
through Wikipedia. The joint syntactic/semantic
model is realized by means of
tree kernels, which can exploit lexical relatedness
to match syntactically similar structures,
i.e. whose lexical compounds are related. The
comparative experiments across different RTE
challenges and traditional systems show that
our approach consistently and meaningfully
achieves high accuracy, without requiring any
adaptation or tuning
Automatic learning of textual entailments with cross-pair similarities
Full text linkIn this paper we define a novel similarity
measure between examples of textual entailments and we use it as a kernel function in Support Vector Machines (SVMs).
This allows us to automatically learn the
rewrite rules that describe a non trivial set
of entailment cases. The experiments with
the data sets of the RTE 2005 challenge
show an improvement of 4.4% over the state-of-the-art methods
Experimenting a "general purpose" textual entailment learner in AVE
Full text linkIn this paper we present the use of a "general purpose" textual entailment recognizer in the Answer Validation Exercise (AVE) task. Our system is designed to learn entailment rules from annotated examples. Its main feature is the use of Support Vector Machines (SVMs) with kernel functions based on cross-pair similarity between entailment pairs. We experimented with our system using different training sets: RTE and AVE data sets. The comparative results show that entailment rules can be learned. Although, the high variability of the outcome prevents us to derive definitive conclusions, the results show that our approach is quite promising and improvable in the future. © Springer-Verlag Berlin Heidelberg 2007
Fast and Effective Kernels for Relational Learning from Texts
Full text linkIn this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such models by optimizing the dynamic programming algorithm of the kernel evaluation. Experiments with Support Vector Machines and the above kernels show the effectiveness and efficiency of our approach on two very important natural language tasks, Textual Entailment Recognition and Question Answering. 1
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