Label Propagation

Shortage of manually sense-tagged data is an obstacle to supervised word sense dis- ambiguation methods. In this paper we in- vestigate a label propagation based semi- supervised learning algorithm for …...

Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natu- ral language processing. All graph-based algorithms rely on a graph that jointly rep- resents …...

This paper proposes a semi-supervised learn- ing method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate …...

We present a graph-based semi-supervised la- bel propagation algorithm for acquiring open- domain labeled classes and their instances from a combination of unstructured and struc- tured text sources. This acquisition …...

We present an extensive study on the prob- lem of detecting polarity of words. We consider the polarity of a word to be ei- ther positive or negative. For example, …...

We describe a new approach to disambiguat- ing semantic frames evoked by lexical predi- cates previously unseen in a lexicon or anno- tated data. Our approach makes use of large …...

. With the advent of consumer generated media (e.g., Amazon reviews, Twitter, etc.), sentiment classification becomes a heated topic. Previous work heavily relies on a large amount of linguistic resources, …...

This paper introduces a graph-based semi- supervised joint model of Chinese word segmentation and part-of-speech tagging. The proposed approach is based on a graph-based label propagation technique. One constructs a …...

The lack of labeled data always poses challenges for tasks where machine learn- ing is involved. Semi-supervised and cross-domain approaches represent the most common ways to overcome this dif- ficulty. …...

Because of polysemy, distant labeling for information extraction leads to noisy train- ing data. We describe a procedure for re- ducing this noise by using label propaga- tion on a …...

AbstractIn the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled …...

In this paper, we show how distributionally-induced semantic classes can be helpful for extracting hypernyms. We present methods for inducing sense-aware semantic classes using distributional semantics and using these induced …...

AbstractRecently, distant supervision has gained great success on Fine-grained Entity Typing (FET). Despite its efficiency in reducing manual labeling efforts, it also brings the challenge of dealing with false entity …...

AbstractPart-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource languages lack labeled data for training. An established method for training a POS tagger in such …...

AbstractEntity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step to bridging and integrating multi-source KGs. In this paper, we argue that existing complex …...