Many machine learning and data mining algorithms on the similarity metrics. The Cosine similarity, wh the inner product of two normalized feature vectors, most commonly used similarity measures. Howev practical tasks such as text categorization an clustering, the Cosine similarity is calculated assumption that the input space is an orthogonal usually could not be satisfied due to synonymy an Va...

For the purpose of opinion exploring in tweets, this article presents a sentiment classification of tweets content. First, we present a method to identify new sentiment similarity seed words. These seed words are used for predicting sentiment intensity of other words and short phrases in co-occurrence. Then, for testing sentiment similarity, we use: Similarity Measures methods between words and...

In three studies we investigated whether LSA cosine values estimate human similarity ratings of word pairs. In study 1 we found that LSA can distinguish between highly similar and dissimilar matches to a target word, but that it does not reliably distinguish between highly similar and less similar matches. In study 2 we showed that, using an expanded item set, the correlation between LSA rating...

A description of a system for measuring the compositionality of collocations within the framework of the shared task of the Distributional Semantics and Compositionality workshop (DISCo 2011) is presented. The system exploits the intuition that a highly compositional collocation would tend to have a considerable semantic overlap with its constituents (headword and modifier) whereas a collocatio...

State-of-the-art story link detection systems, that is, systems that determine whether two stories are about the same event or linked, are usually based on the cosine-similarity measured between two stories. This paper presents a method for improving the performance of a link detection system by using a variety of similarity measures and using source-pair specific statistical information. The u...

Sentiment analysis is a data processing to recognize topics that people talk about and their sentiments toward the topics, one of which in this study large-scale social restrictions (PSBB). This aims classify negative positive by applying K-Nearest Neighbor algorithm see accuracy value 3 types distance calculation are cosine similarity, euclidean, manhattan for Indonesian language tweets (PSBB)...

Similarity: measure of how close to each other two instances are. The “closer” the instances are to each other, the larger is the similarity value. Dissimilarity: measure of how different two instances are. Dissimilarity is large when instances are very different and is small when they are close. Proximity: refers to either similarity or dissimilarity Distance metric: a measure of dissimilarity...

In this paper, we investigate the use of temporal information for improving extractive summarization of historical articles. Our method clusters sentences based on their timestamps and temporal similarity. Each resulting cluster is assigned an importance score which can then be used as a weight in traditional sentence ranking techniques. Temporal importance weighting offers consistent improveme...

The technology of business process management is being more widely used, and there are more and more business process models. In this demonstration, we show how to query a large number of models efficiently with BeehiveZ. Four types of queries are all supported in BeehiveZ, i.e. exact query based on structure, similarity query based on structure, exact query based on behavior, similarity query ...

Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, including better capturing uncertainty about a representation and its relationships, expressing asymmetries more naturally than dot product or cosine similarity, and enabling more expressive parameterization of decision...