Burgeoning research in applied linguistics has underscored the interplay among individual, cognitive, and social variables that can delineate the ultimate attainment in various areas including vocabulary learning and the need to explore how innovative conflation of these dimensions may promote learning outcomes. The present quasi-experimental study examined the impact of Thematic Vocabulary Ins...

In this paper we consider whether the thematic document clustering approach of Contextual Document Clustering is able to capture the overall sentiment of a cluster of documents. We provide a novel mechanism to determine the sentiment of a cluster based on the latter approach and assess the approach on three data sets formed from the NY Times annotated corpus. We demonstrate that CDC does provid...

Semantic structure in the mental lexicon is often assumed to follow a taxonomic structure grouping similar items. This study uses a network clustering analysis of a massive word association dataset that does not primarily focus on concrete noun categories, but includes the majority of the words used in daily life. At this scale, we found widespread overlap between thematically organized cluster...

We propose a method that incorporates paraphrase information from the Web to boost the performance of a supervised relation extraction system. Contextual information is extracted from the Web using a semi-supervised process, and summarized by skip-bigram overlap measures over the entire extract. This allows the capture of local contextual information as well as more distant associations. We obs...

Automatically identifying the event type of event-related information in the sheer amount of social media data makes machine learning inevitable. However, this is highly dependent on (1) the number of correctly labeled instances and (2) labeling costs. Active learning has been proposed to reduce the number of instances to label. Albeit the thematic dimension is already used, other metadata such...

As the volume of online manufacturing information grows steadily, the need for developing dedicated computational tools for information organization and mining becomes more pronounced. This paper proposes a novel approach for facilitating search and organization of textual documents and also extraction of thematic patterns in manufacturing corpora using document clustering and topic modeling te...

The ever-increasing information on the web with its heterogeneity and dynamism needs an information retrieval system which serves searcher’s ambiguous, ill-formed, short queries with relevant result in a precise way. Web search result clustering has been emerged as a method which overcomes these drawbacks of conventional information retrieval (IR) systems. It is the clustering of results return...

In this paper we present a novel algorithm for document clustering. This approach is based on distributional clustering where subject related words, which have a narrow context, are identified to form metatags for that subject. These contextual words form the basis for creating thematic clusters of documents. We believe that this approach will be invaluable in creating an information retrieval ...

In this paper we show how approximate matrix factorisations can be used to organise document summaries returned by a search engine into meaningful thematic categories. We compare four different factorisations (SVD, NMF, LNMF and K-Means/Concept Decomposition) with respect to topic separation capability, outlier detection and label quality. We also compare our approach with two other clustering ...

In this paper, the authors describe and implement an algorithm to perform a supervised classification into Landsat MSS satellite images. The Maximum Likelihood Classification method is used to generate raster digital thematic maps by means of a supervised clustering. The clustering method has been proved in Landsat MSS images of different regions of Mexico to detect several training data relate...