This paper presents two approaches to semantic search by incorporating Linked Data annotations of documents into a Generalized Vector Space Model. One model exploits taxonomic relationships among entities in documents and queries, while the other model computes term weights based on semantic relationships within a document. We publish an evaluation dataset with annotated documents and queries a...

This paper proposes a new approach to domain adaptation in statistical machine translation (SMT) based on a vector space model (VSM). The general idea is first to create a vector profile for the in-domain development (“dev”) set. This profile might, for instance, be a vector with a dimensionality equal to the number of training subcorpora; each entry in the vector reflects the contribution of a...

This research proposes a novel technique for fake news classification using natural language processing (NLP) methods. The proposed technique, TwIdw (Term weight–inverse document weight), is used feature extraction and based on TfIdf, with the term frequencies replaced by depth of words in documents. effectiveness compared to another method—basic TfIdf. Classification models were created random...

This paper introduces a compositional semantics of locative prepositional phrases which is based on a vector space ontology. Model-theoretic properties of prepositions like monotonicity and conservativity are defined in this system in a straightforward way. These notions are shown to describe central inferences with spatial expressions and to account for the grammaticality of preposition modifi...

Although many of the existing adaptive learning environments use other approaches, the vector-space model for information retrieval can be used in providing individualized learning with hypermedia. A system employing a modified version of the vector-space model is described. The approach taken is tested in a real-life setting to evaluate and train basic arithmetics skills of learners in element...

In information retrieval research; Genetic Algorithms (GA) can be used to find global solutions in many difficult problems. This study used different similarity measures (Dice, Inner Product) in the VSM, for each similarity measure we compared ten different GA approaches based on different fitness functions, different mutations and different crossover strategies to find the best strategy and fi...

This paper presents the architecture of the Concept-based Vector Space Model Question Answering System (CBVSM-QAS) developed at the Nagaoka University of Technology (NUT) and used in the 4-th NTCIR workshop Question Answering Challenge (QAC) evaluation. The CBVSM-QAS runs on the factual question, which is, correspond to the subtask-1 in the NTCIR-4’s QAC. One major peculiarity of this system is...

Query expansion is an effective way to extend the coverage of retrieval to the related documents. Various approaches have been proposed and many of them are based o vector space model. The expansion process consists of simply adding expansion terms into the original vector. In this paper we argue that this simplistic expansion method can bias the focus of the original query, because the expande...

In this paper, we present an original framework to model frame semantic resources (namely, FrameNet) using minimal supervision. This framework can be leveraged both to expand an existing FrameNet with new knowledge, and to induce a FrameNet in a new language. Our hypothesis is that a frame semantic resource can be modeled and represented by a suitable semantic space model. The intuition is that...

Using i-vector space features has been shown to be very successful in speaker and language identification. In this paper, we evaluate using the i-vector framework for emotion recognition from speech. Instead of using standard i-vector features, we propose to use concatenated emotion specific i-vector features. For each emotion category, a GMM supervector is generated via adaptation of the neura...