. The term big data has come into use in recent years. It is used to refer to the ever-increasing amount of information that organisations are storing, processing and analysing. Owing to the growing number of information sources big data based file systems are necessary. Due to the explosion growth of digital information, automatic document clustering or categorization has become more important...

Assessing verbal output in category fluency tasks provides a sensitive indicator of cortical dysfunction. The most common metrics are the overall number of words produced and the number of errors. Two main observations have been made about the structure of the output, first that there is a temporal component to it with words being generated in spurts, and second that the clustering pattern may ...

Measuring semantic similarity between words is vital for various applications in natural language processing, such as language modeling, information retrieval, and document clustering. We propose a method that utilizes the information available on the Web to measure semantic similarity between a pair of words or entities. We integrate page counts for each word in the pair and lexico-syntactic p...

Although HIV is detectable throughout the CNS, its preferential disruption of fronto-basal ganglia systems is thought to underlie the neurobehavioral syndrome of HIV-associated dementia. Semantic clustering, a measure of organizational strategy during learning and retrieval, is commonly impaired in patients with frontal systems dysfunction, but has not previously been evaluated in HIV-associate...

In this thesis we propose the Semantic Tag Clustering Search framework (STCS). This framework consists of three parts. The first part deals with syntactic variations by clustering tags that are syntactic variations of each other and assigning a label to them. The second part of the framework addresses the problem of recognizing homonyms and identifying semantically related tags. The last, and f...

In order to generate semantic annotations for a collection of documents, one needs an annotation schema consisting of a semantic model (a.k.a. ontology) along with lists of linguistic indicators (keywords and patterns) for each concept in the ontology. The focus of this paper is the automatic generation of the linguistic indicators for a given semantic model and a corpus of documents. Our appro...

Web transaction data between web visitors and web functionalities usually convey users’ task-oriented behavior patterns. Clustering web transactions, thus, may capture such informative knowledge, in turn, build user profiles, which are associated with different navigational patterns. For some advanced web applications, such as web recommendation or personalization, the aforementioned work is cr...

We present our experience in applying distributional semantics (neural word embeddings) to the problem of representing and clustering documents in a bilingual comparable corpus. Our data is a collection of Russian and Ukrainian academic texts, for which topics are their academic fields. In order to build language-independent semantic representations of these documents, we train neural distribut...

Clustering hashtags based on their semantics is an important problem with many applications. The uncontrolled usage of hashtags in social media, however, makes the quality of semantics and the frequency of usage vary a lot, and this poses a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag (by using metadata) or the contextual semantics of a hasht...

In this paper the GK` model for the construction of thesaurus classes based on fuzzy semantic association measure between index terms and concepts (thesaurus classes) is presented. The association measure is obtained on the basis of fuzzy semantic relations between