The purpose of this paper is to make a detailed study of vector spaces and a certain vector-like system.

Learning and representing semantics is one of the most important tasks that significantly contribute to some growing areas, as successful stories in the recent survey of Turney and Pantel (2010). In this thesis, we present an in- novative (and first) framework for creating a multimodal distributional semantic model from state of the art text-and image-based semantic models. We evaluate this multimodal semantic model on simulating similarity judgements, concept clustering and the newly introduced BLESS benchmark. We also propose an effective algorithm, namely Parameter Estimation, to integrate text- and image- based features in order to have a robust multimodal system. By experiments, we show that our technique is very promising. Across all experiments, our best multimodal model claims the first position. By relatively comparing with other text-based models, we are justified to affirm that our model can stay in the top line with other state of the art models. We explore various types of visual features including SIFT and other color SIFT channels in order to have prelim- inary insights about how computer-vision techniques should be applied in the natural language processing domain. Importantly, in this thesis, we show evi- dences that adding visual features (as the perceptual information coming from...

Ve statistických modelů sémantiky jsou významy slov pouze na základě jejich distribuční vlastnosti.Základní zdroj je zde jeden slovník, který lze použít pro různé úkoly, kde se význam slov reprezentovány jako vektory v vektorového prostoru, a slovní podoby jako vzdálenosti mezi jejich vektorových osobnosti. Pomocí silných podobnosti, může vhodnost podmínek uvedených zejména v souvislosti se vypočítá a používá pro celou řadu úkolů, jeden z nich je slovo smysl Disambiguation. V této práci bylo vyšetřeno několik různých přístupů k modelům z vektorového prostoru a prováděny tak, aby k překročení vyhodnocení vlastního výkonu na Word Sense disambiguation úkolem Prague Dependency Treebank.

Secret Sharing refers to a method through which a secret key K can be shared among a group of authorized participants, such that when they come together later, they can figure out the secret key K to decrypt the encrypted message. Any group which is not authorized cannot determine the secret key K. Some of the important secret schemes are Shamir Threshold Scheme, Monotone Circuit Scheme, and Brickell Vector Space Scheme. Brikell’s vector space secret sharing construction requires the existence of a function from a set of participant P in to vector space Zdp, where p is a prime number and d is a positive number. There is no known algorithm to construct such a function in general. We developed an efficient algorithm to construct function for some special secret sharing scheme. We also give an algorithm to demonstrate how a secret sharing scheme can be used in visual cryptography.

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This dissertation develops a semantic model of gradable adjectives such as ‘tall’, ‘good’, ‘big’, ‘heavy’, etc., within a formal semantic theory of locatives we call Locative Structure Semantics (LSS). Our central hypothesis is that gradable adjectives are, semantically, a species of locative expression. The view of gradable adjectives as locatives is inspired by the vector-based semantic models of Vector Space Semantics (VSS), as well as the notion of perspective or point of view, as found in Leonard Talmy’s research on spatial expressions (Talmy [153]) and the tradition of Situation Semantics (cf. Barwise and Perry [9, p. 39]). Following Barwise and Seligman [11], we construe the contextual variability that characterises gradable adjectives in terms of shifts in cognitive perspective. We argue that perspectives are a formal part of a semantic representational structure that is shared by expressions from several different domains, which we refer to as a locative structure (L-structure). The notion of an L-structure is influenced by Reichenbach’s notion of tense, and can be thought of as a generalisation of the Reichenbachian notion of tense to the realm of concepts. Reichenbach [134] proposed that each temporal expression is associated with three time points: a speech point, S, an event point, E, and reference point, R, where E refers to the time point corresponding to the event described by the tensed clause, S is (usually) taken to be the speaker’s time of utterance, and R is a temporal reference point relevant to the utterance. In LSS we extend this tripartite scheme to locative expressions in general, to which we assign a ternary structure comprising a Perspective, a Figure, and a Ground, represented symbolically as P, F, and G, and which are generalisations of the Reichenbachian S, E, and R, respectively. We show that a formal semantics based on L-structures enables us to capture important crosscategorial similarities between gradable adjectives, tenses, and spatial prepositions.

This thesis introduces a general method for incorporating the distributional analysis of textual and linguistic objects into text-to-speech (TTS) conversion systems. Conventional TTS conversion uses intermediate layers of representation to bridge the gap between text and speech. Collecting the annotated data needed to produce these intermediate layers is a far from trivial task, possibly prohibitively so for languages in which no such resources are in existence. Distributional analysis, in contrast, proceeds in an unsupervised manner, and so enables the creation of systems using textual data that are not annotated. The method therefore aids the building of systems for languages in which conventional linguistic resources are scarce, but is not restricted to these languages. The distributional analysis proposed here places the textual objects analysed in a continuous-valued space, rather than specifying a hard categorisation of those objects. This space is then partitioned during the training of acoustic models for synthesis, so that the models generalise over objects' surface forms in a way that is acoustically relevant. The method is applied to three levels of textual analysis: to the characterisation of sub-syllabic units, word units and utterances. Entire systems for three languages (English, Finnish and Romanian) are built with no reliance on manually labelled data or language-specific expertise. Results of a subjective evaluation are presented.

This thesis analyses use of vector-space model and data clustering approaches on parts of single document - on chapters, paragraphs and sentences. A simulation application (SimDIS), written in C# programming language is also part of this thesis. The application implements the adjusted model and provides tools for visualization of vectors and clusters.

This thesis analyses use of vector-space model and data clustering approaches on parts of single document - on chapters, paragraphs and sentences - to allow simple navigation between similar parts. A simulation application (SimDIS), written in C# programming language is also part of this thesis. The application implements the described model and provides tools for visualization of vectors and clusters.

Legacy systems contain critical and complex business code that has been in use for a long time. This code is difficult to understand, maintain, and evolve, in large part due to crosscutting concerns: software system features, such as persistence, logging, and error handling, whose implementation is spread across multiple modules. Aspect-oriented techniques separate crosscutting concerns from the base code, using separate modules called aspects and, thus, simplifying the legacy code. Aspect mining techniques identify aspect candidates so that the legacy code can be refactored into aspects. This study investigated an automated aspect mining method in which a vector-space model clustering approach was used with model-based clustering. The vector-space model clustering approach has been researched for aspect mining using a number of different heuristic clustering methods and producing mixed results. Prior to this study, this model had not been researched with model-based algorithms, even though they have grown in popularity because they lend themselves to statistical analysis and show results that are as good as or better than heuristic clustering methods. This study investigated the effectiveness of model-based clustering for identifying aspects when compared against heuristic methods, such as k-means clustering and agglomerative hierarchical clustering, using six different vector-space models. The study's results indicated that model-based clustering can, in fact, be more effective than heuristic methods and showed good promise for aspect mining. In general, model-based algorithms performed better in not spreading the methods of the concerns across the multiple clusters but did not perform as well in not mixing multiple concerns in the same cluster. Model-based algorithms were also significantly better at partitioning the data such that, given an ordered list of clusters, fewer clusters and methods would need to be analyzed to find all the concerns. In addition, model-based algorithms automatically determined the optimal number of clusters, which was a great advantage over heuristic-based algorithms. Lastly, the study found that the new vector-space models performed better, relative to aspect mining, than previously defined vector-space models.