We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing models for this task do not take syntactic structure sufficiently into account. We present a novel structured vector space model ...

Over the last quarter-century, there is increasing body of research on understanding the human emotions. In this study, automatic classification of anger, disgust, fear, joy and sad emotions in text have been studied on the ISEAR (International Survey on Emotion Antecedents and Reactions) dataset. For the classification we have used Vector Space Model with a total of 801 news headlines provided...

This paper introduces the task of Chinese personal name disambiguation of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing (CLP) 2012 that Natural Language Processing Laboratory of Zhengzhou University took part in. In this task, we mainly use the Vector Space Model to disambiguate Chinese personal name. We extract different named entity features from diverse names informa...

Cross-document coreference occurs when the same person, place, event, or concept is discussed in more than one text source. Computer recognition of this phenomenon is important because it helps break "the document boundary" by allowing a user to examine information about a particular entity from multiple text sources at the same time. In this paper we describe a cross-document coreference resol...

Classification of text documents presents a unique challenge to conventional classification algorithms. Due to the existence of large number of features in the datasets, providing a desired representation for text documents can be seen as another problem. In this paper a simple but effective representation model for text documents to tackle the classification problem is discussed. Two different...

Syntactic comparison across languages is essential in the research field of linguistics, e.g. when investigating the relationship among closely related languages. In IR and NLP, the syntactic information is used to understand the meaning of word occurrences according to the context in which their appear. In this paper, we discuss a mathematical framework to compute the distance between language...

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 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...