Sentiment analysis or opinion mining in online product reviews is a method that can automatically detect subjective information regarding the entity such as opinions, attitudes, and feelings expressed by consumers. Online product reviews always include objective and subjective sentences; identification of subjective sentences in the given content is a very important and foundational task in the...

As a recent emerging technique, semi-supervised clustering has attracted significant research interest. Compared to traditional clustering algorithms, which only use unlabeled data, semi-supervised clustering employs both unlabeled and supervised data to obtain a partitioning that conforms more closely to the user's preferences. Several recent papers have discussed this problem (Cohn, Caruana, ...

For many machine learning applications, labeled data may be very difficult or costly to obtain. For instance in the case of speech analysis, the average annotation time for a one hour telephone conversation transcript is 400 hours.[7] To circumvent this problem, one can use semi-supervised learning algorithms which utilize unlabeled data to improve performance on a supervised learning task. Sin...

We present a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. Our system combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text. Understanding via empirical experimentation how to effectively combine various types of clustering features allows us to seamless...

We propose a new formulation called hyperedge expansion (HE) for hypergraph learning. The HE expansion transforms the hypergraph into a directed graph on the hyperedge level. Compared to the existing works (e.g. star expansion or normalized hypergraph cut), the learning results with HE expansion would be less sensitive to the vertex distribution among clusters, especially in the case that clust...

Independent Factor Analysis (IFA) is used to recover latent components (or sources) from their linear observed mixtures within an unsupervised learning framework. Both the mixing process and the source densities are learned from the observed data. The sources are assumed to be mutually independent and distributed according to a mixture of Gaussians. This paper investigates the possibility of in...

Document level sentiment analysis, the task of determining whether the sentiment expressed in a document is positive or negative, is commonly performed by supervised methods. As with all supervised tasks, obtaining training data for these methods can be expensive and timeconsuming. Some semi-supervised approaches have been proposed that rely on sentiment lexicons. We propose a novel supervised ...

Semi-supervised learning, which uses a small amount of labeled data in conjunction with a large amount of unlabeled data for training, has recently attracted huge research attention due to the considerable improvement in learning accuracy. In this work, we focus on semisupervised variable weighting for clustering, which is a critical step in clustering as it is known that interesting clustering...

In recent years, enteromorpha prolifera detection has received increasing attention. Supervised learning with remote sensing images can achieve satisfactory performances for green tide monitoring. However, data distributions between images obviously differ, and it would be too costly to label a massive amount of images for enteromorpha prolifera detection. Thus, this paper focuses on detecting ...

We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every latent variable has an “anchor”, an observed variable with only that latent variable as its parent. Given such anchors, we show that it is possible to consistently recover moments of the latent variables and use these mom...