Automatic Subjective Question Answering (ASQA), which aims at answering users’ subjective questions using summaries of multiple opinions, becomes increasingly important. One challenge of ASQA is that expected answers for subjective questions may not readily exist in theWeb. The rising and popularity of Community Question Answering (CQA) sites, which provide platforms for people to post and answ...

Several methods for automatically generating labeled examples that can be used as training data for WSD systems have been proposed, including a semisupervised approach based on relevance feedback (Stevenson et al., 2008a). This approach was shown to generate examples that improved the performance of a WSD system for a set of ambiguous terms from the biomedical domain. However, we find that this...

Abstract: Resistance testing is an important tool in today’s anti-HIV therapy management for improving the success of antiretroviral therapy. Routinely, the genetic sequence of viral target proteins is obtained. These sequences are then inspected for mutations that might confer resistance to antiretroviral drugs. However, interpretation of the genomic data is challenging. In recent years, appro...

Considerable progress was recently made on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabeled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper suggests a space of basis functions to perform semi-supervised inductive learning. As a nice pr...

We present a framework to address the imbalanced data problem using semi-supervised learning. Specifically, from a supervised problem, we create a semi-supervised problem and then use a semi-supervised learning method to identify the most relevant instances to establish a welldefined training set. We present extensive experimental results, which demonstrate that the proposed framework significa...

Recognizing speech act types in Twitter is of much theoretical interest and practical use. Our previous research did not adequately address the deficiency of training data for this multi-class learning task. In this work, we set out by assuming only a small seed training set and experiment with two semi-supervised learning schemes, transductive SVM and graph-based label propagation, which can l...

There tends to be a substantial proportion of reviews that include explicit textual comparisons between the reviewed item and another product. To the extent that such comparisons can be captured reliably by automatic means, they can provide an extremely helpful input to support a process of choice. As the small amount of available training data limits the development of robust systems to automa...

Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performance of a learning task, in the sense that fewer labeled training data are needed to achieve a target error bound. However, in other situations unlabeled data do not seem to help. Recent attempts at theoretically charact...

We evaluate two very different methods for domain adaptation of graph-based dependency parsers on the EVALITA 2011 Domain Adaptation data, namely instance-weighting [10] and self-training [9, 6]. Since the source and target domains (newswire and law, respectively) were very similar, instance-weighting was unlikely to be efficient, but some of the semi-supervised approaches led to significant im...

The classification performance of the learner is weakened when unlabeled examples are mislabeled during co-training process. A semisupervised co-training algorithm based on assisted learning (AR-Tri-training) was proposed. Firstly, the assisted learning strategy was presented, which is combined with rich information strategy for designing the assisted learner. Secondly, the evaluation factor wa...