The soft cardinality proved to be a very strong text-overlapping baseline for the task of semantic-textual-similarity (STS) obtaining the third place in SemEval-2012. This year, besides to the plain text-overlapping approach, two distributional word-similarity functions derived from the ukWack corpus were tested within the soft cardinality. These measures contributed to improve the performance ...

Text similarity has a relatively wide range of applications in many fields, such as intelligent information retrieval, question answering system, text rechecking, machine translation, and so on. The text similarity computing based on the meaning has been used more widely in the similarity computing of the words and phrase. Using the knowledge structure of the and its method of knowledg...

This paper presents a methodology for interacting with images using both text and visual features. When images are entered into the database, they are associated with a piece of text (typically a web page), which is used to derive a lexical index for the image. At the same time, visual features are computed. The similarity between two images is then a combination between their visual similarity...

Traditional text similarity measures consider each term similar only to itself and do not model semantic relatedness of terms. We propose a novel discriminative training method that projects the raw term vectors into a common, low-dimensional vector space. Our approach operates by finding the optimal matrix to minimize the loss of the pre-selected similarity function (e.g., cosine) of the proje...

Similarity searching in text databases with multiple field types is still an open problem. We focus our attention on the “COmmunity Research and Development Information Service” (CORDIS) database of the European Union and we evaluate the effectiveness of many text retrieval methods in terms of precision, recall and ranking quality. Our experiments indicate that different field types should be h...

Semantic text similarity (STS) is an essential problem in many Natural Language Processing tasks, which has drawn a considerable amount of attention by research community in recent years. In this paper, our work focused on computing semantic similarity between texts of sentence length. We employed a Support Vector Regression model with rich effective features to predict the similarity scores be...

Sentence and document similarity assessment is key to most NLP applications. This paper presents a novel measure of calculating the similarity between sentences or between documents using ontology. The similarity is assessed using sentence or document concept vector forming from finding the linkage between ontology terms and sentence or document content, the linage can be used to generate seman...

This paper proposes a maximum-minimum similarity training algorithm to optimize the parameters in the effective method of text extraction based on Gaussian mixture modeling of neighbor characters. The maximum-minimum similarity training (MMS) methods optimize recognizer performance through maximizing the similarities of positive samples and minimizing the similarities of negative samples. Based...

Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Partitional clustering algorithms have been recognized to be more suitable as opposed to the hierarchical clustering schemes for processing large datasets....

This paper describes the University of Sheffield’s submission to SemEval-2012 Task 6: Semantic Text Similarity. Two approaches were developed. The first is an unsupervised technique based on the widely used vector space model and information from WordNet. The second method relies on supervised machine learning and represents each sentence as a set of n-grams. This approach also makes use of inf...