We propose the weakly supervised MultiExperts Model (MEM) for analyzing the semantic orientation of opinions expressed in natural language reviews. In contrast to most prior work, MEM predicts both opinion polarity and opinion strength at the level of individual sentences; such fine-grained analysis helps to understand better why users like or dislike the entity under review. A key challenge in...

Sensor-based human activity recognition aims to automatically identify human activities from a series of sensor observations, which is a crucial task for supporting wide range applications. Typically, given sufficient training examples for all activities (or activity classes), supervised learning techniques have been applied to build a classification model using sufficient training samples for ...

We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to ...

Traditional immune intrusion detection algorithms need lots of labeled training data. However, it is difficult to obtain sufficient labeled data in real situation. In this paper we present a semi-supervised clustering based immune intrusion detection algorithm called SCIID, which can improve the quality of antibodies constantly and enhance the detection rate. Experimental results show that SCII...

Machine learning has enjoyed astounding practical success in a wide range of applications in recent years—practical success that often hurries ahead of our theoretical understanding. The standard framework for machine learning theory assumes full supervision, that is, training data consists of correctly labeled i.i.d. examples from the same task that the learned classifier is supposed to be app...

This paper describes our event extraction system that participated in the bacteria biotopes task in BioNLP Shared Task 2011. The system performs semi-supervised named entity recognition by leveraging additional information derived from external resources including a large amount of raw text. We also perform coreference resolution to deal with events having a large textual scope, which may span ...

Traditional supervised classifiers use only labeled data (features/label pairs) as the training set, while the unlabeled data is used as the testing set. In practice, it is often the case that the labeled data is hard to obtain and the unlabeled data contains the instances that belong to the predefined class beyond the labeled data categories. This problem has been widely studied in recent year...

We introduce cause identification, a new problem involving classification of incident reports in the aviation domain. Specifically, given a set of pre-defined causes, a cause identification system seeks to identify all and only those causes that can explain why the aviation incident described in a given report occurred. The difficulty of cause identification stems in part from the fact that it ...

In a multi-class document categorization using graph-based semi-supervised learning (GBSSL), it is essential to construct a proper graph expressing the relation among nodes and to use a reasonable categorization algorithm. Furthermore, it is also important to provide high-quality correct data as training data. In this context, we propose a method to construct a similarity graph by employing bot...

Automatic text classification has a long history and many studies have been conducted in this field. In particular, many machine learning algorithms and information retrieval techniques have been applied to text classification tasks. Even though much technical progress has been made in text classification, there is still room for improvement in text classification. In this paper, we will discus...