Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model

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cross pair text representations for answer sentence selection

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Reltextrank: An open source framework for building relational syntactic-semantic text pair representations

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Теорія та практика менеджменту безпеки

У збірнику подано тези доповідей та виступів учасників Міжнародної науково-практичної конференції, присвяченої питанням теорії менеджменту безпеки, безпеки особистості, прикладним аспектам забезпечення соціальної, екологічної, економічної безпеки підприємств, питанням механізму забезпечення соціоекологоекономічної безпеки регіону, проблемам забезпечення національної безпеки

A General Framework for Exploiting Background Knowledge in Natural Language Processing

The two key aspects of natural language processing (NLP) applications based on machine learning techniques are the learning algorithm and the feature representation of the documents, entities, or words that have to be manipulated. Until now, the majority of the approaches exploited syntactic features, while semantic feature extraction suffered from low coverage of the available knowledge resources and the difficulty to match text and ontology elements. Nowadays, the Semantic Web made available a large amount of logically encoded world knowledge called Linked Open Data (LOD). However, extending state-of-the-art natural language applications to use LOD resources is not a trivial task due to a number of reasons, including natural language ambiguity and heterogeneity and ambiguity of the schemes adopted by different LOD resources. In this thesis we define a general framework for supporting NLP with semantic features extracted from LOD. The main idea behind the framework is to (i) map terms in text to the unique resource identifiers (URIs) of LOD concepts through Wikipedia mediation; (ii) use the URIs to obtain background knowledge from LOD; (iii) integrate the obtained knowledge as semantic features into machine learning algorithms. We evaluate the framework by means of case studies on coreference resolution and relation extraction. Additionally, we propose an approach for increasing accuracy of the mapping step based on the "one sense per discourse" hypothesis. Finally, we present an open-source Java tool for extracting LOD knowledge through SPARQL endpoints and converting it to NLP features

Fbkirst: Semantic relation extraction using cyc

We present an approach for semantic relation extraction between nominals that combines semantic information with shallow syntactic processing. We propose to use the ResearchCyc knowledge base as a source of semantic information about nominals. Each source of information is represented by a specific kernel function. The experiments were carried out using support vector machines as a classifier. The system achieves an overall F1 of 77.62 % on the “Multi-Way Classificatio

Encoding Semantic Resources in Syntactic Structures for Passage Reranking

In this paper, we propose to use seman-tic knowledge from Wikipedia and large-scale structured knowledge datasets avail-able as Linked Open Data (LOD) for the answer passage reranking task. We represent question and candidate answer passages with pairs of shallow syntac-tic/semantic trees, whose constituents are connected using LOD. The trees are pro-cessed by SVMs and tree kernels, which can automatically exploit tree fragments. The experiments with our SVM rank algo-rithm on the TREC Question Answering (QA) corpus show that the added relational information highly improves over the state of the art, e.g., about 15.4 % of relative im-provement i

Wikipedia-based WSD for Multilingual Frame Annotation

none3Sara Tonelli; Claudio Giuliano; Kateryna TymoshenkoTonelli, Sara; Giuliano, Claudio; Tymoshenko, Kateryn

Using background knowledge to support coreference resolution

Systems based on statistical and machine learning methods have been shown to be extremely effective and scalable for the analysis of large amount of textual data. However, in the recent years, it becomes evident that one of the most important direction of improvement in natural language processing (NLP) tasks, like word sense disambiguation, coreference resolution, relation extraction, and other tasks related to knowledge extraction, is by exploiting semantics. While in the past, the unavailability of rich and complete semantic descriptions constituted a serious limitation of their applicability, nowadays, the Semantic Web made available a large amount of logically encoded information (e.g. ontologies, RDF(S)-data, linked data, etc.), which constitute a valuable source of semantics. However, web semantics cannot be easily plugged into machine learning systems. Therefore the objective of this paper is to define a reference methodology for combining semantics information available in the web under the form of logical theories, with statistical methods for NLP. The major problems that we have to solve to implement our methodology concern (i) the selection of the correct and minimal knowledge among the large amount available in the web, (ii) the representation of uncertain knowledge, and (iii) the resolution and the encoding of the rules that combine knowledge retrieved from Semantic Web sources with semantics in the text. In order to evaluate the appropriateness of our approach, we present an application of the methodology to the problem of intra-document coreference resolution, and we show by means of some experiments on the ACE 2005 dataset, how the injection of knowledge is correlated to the improvement of the performance of our approach on this tasks