This paper describes the use of a bilingual vector model for the automatic discovery of German translations of English terms. The model is built by analysing co-occurence patterns in a parallel corpus of English and German medical abstracts, a method also used for CrossLingual Information Retrieval. The model generates candidate German translations of English words using the cosine similarity m...

This paper describes the DSTO/Macquarie University System for Entity Linking (DAMSEL), which competed in the 2009 Text Acquisition Conference Knowledge Base Population task. The system achieves 73.5% accuracy. For a given named entity mention, the system selects a set of candidate entities from the knowledge base and selects the most likely candidate based on the similarity between the document...

The purpose of this study is to propose new similarity measures namely rough variational coefficient similarity measure under the rough neutrosophic environment. The weighted rough variational coefficient similarity measure has been also defined. The weighted rough variational coefficient similarity measures between the rough ideal alternative and each alternative are xxxxx calculated to find t...

Distributional semantics tries to characterize the meaning of words by the contexts in which they occur. Similarity of words hence can be derived from the similarity of contexts. Contexts of a word are usually vectors of words appearing near to that word in a corpus. It was observed in previous research that similarity measures for the context vectors of two words depend on the frequency of the...

In recent years we have experienced an increase in the usage of tags to describe resources. However, the free nature of tagging presents some challenges regarding the search and exploration of tag spaces. In order to deal with these challenges we propose the Semantic Tag Clustering Search (STCS) framework. The framework first groups syntactic variations using several measures based on the Leven...

Word embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for analogical similarities. In this paper, we explore the potential of pre-trained word embeddings to identify generic types of semantic relations in an unsupervised experiment. We...

Keeping in consideration the high demand for clustering, this paper focuses on understanding and implementing K-means clustering using two different similarity measures. We have tried to cluster the documents using two different measures rather than clustering it with Euclidean distance. Also a comparison is drawn based on accuracy of clustering between fuzzy and cosine similarity measure. The ...

Discovering correspondences between schema elements is a crucial task for data integration. Most matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matchin...

Collaborative Filtering(CF) is a widely accepted method of creating recommender systems. CF is based on the similarities among users or items. Measures of similarity including the Pearson Correlation Coefficient and the Cosine Similarity work quite well for explicit ratings, but do not capture real similarity from the ratings derived from implicit feedback. This paper identifies some problems t...

Owing to the rapidly growing multimedia content available on the Internet, extractive spoken document summarization, with the purpose of automatically selecting a set of representative sentences from a spoken document to concisely express the most important theme of the document, has been an active area of research and experimentation. On the other hand, word embedding has emerged as a newly fa...