4,838 research outputs found
Structured Learning for Taxonomy Induction with Belief Propagation
We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation. Our model incorporates heterogeneous re-lational evidence about both hypernymy and siblinghood, captured by semantic features based on patterns and statistics from Web n-grams and Wikipedia ab-stracts. For efficient inference over tax-onomy structures, we use loopy belief propagation along with a directed span-ning tree algorithm for the core hyper-nymy factor. To train the system, we ex-tract sub-structures of WordNet and dis-criminatively learn to reproduce them, us-ing adaptive subgradient stochastic opti-mization. On the task of reproducing sub-hierarchies of WordNet, our approach achieves a 51 % error reduction over a chance baseline, including a 15 % error re-duction due to the non-hypernym-factored sibling features. On a comparison setup, we find up to 29 % relative error reduction over previous work on ancestor F1.
Taxonomy Induction using Hypernym Subsequences
We propose a novel, semi-supervised approach towards domain taxonomy
induction from an input vocabulary of seed terms. Unlike all previous
approaches, which typically extract direct hypernym edges for terms, our
approach utilizes a novel probabilistic framework to extract hypernym
subsequences. Taxonomy induction from extracted subsequences is cast as an
instance of the minimumcost flow problem on a carefully designed directed
graph. Through experiments, we demonstrate that our approach outperforms
stateof- the-art taxonomy induction approaches across four languages.
Importantly, we also show that our approach is robust to the presence of noise
in the input vocabulary. To the best of our knowledge, no previous approaches
have been empirically proven to manifest noise-robustness in the input
vocabulary
Multiresolutional models of uncertainty generation and reduction
Kolmogorov's axiomatic principles of the probability theory, are reconsidered in the scope of their applicability to the processes of knowledge acquisition and interpretation. The model of uncertainty generation is modified in order to reflect the reality of engineering problems, particularly in the area of intelligent control. This model implies algorithms of learning which are organized in three groups which reflect the degree of conceptualization of the knowledge the system is dealing with. It is essential that these algorithms are motivated by and consistent with the multiresolutional model of knowledge representation which is reflected in the structure of models and the algorithms of learning
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
We present a novel end-to-end reinforcement learning approach to automatic
taxonomy induction from a set of terms. While prior methods treat the problem
as a two-phase task (i.e., detecting hypernymy pairs followed by organizing
these pairs into a tree-structured hierarchy), we argue that such two-phase
methods may suffer from error propagation, and cannot effectively optimize
metrics that capture the holistic structure of a taxonomy. In our approach, the
representations of term pairs are learned using multiple sources of information
and used to determine \textit{which} term to select and \textit{where} to place
it on the taxonomy via a policy network. All components are trained in an
end-to-end manner with cumulative rewards, measured by a holistic tree metric
over the training taxonomies. Experiments on two public datasets of different
domains show that our approach outperforms prior state-of-the-art taxonomy
induction methods up to 19.6\% on ancestor F1.Comment: 11 Pages. ACL 2018 Camera Read
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
A type description is a succinct noun compound which helps human and machines
to quickly grasp the informative and distinctive information of an entity.
Entities in most knowledge graphs (KGs) still lack such descriptions, thus
calling for automatic methods to supplement such information. However, existing
generative methods either overlook the grammatical structure or make factual
mistakes in generated texts. To solve these problems, we propose a
head-modifier template-based method to ensure the readability and data fidelity
of generated type descriptions. We also propose a new dataset and two automatic
metrics for this task. Experiments show that our method improves substantially
compared with baselines and achieves state-of-the-art performance on both
datasets.Comment: ACL 201
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