Semi-Supervised Induction

Word Sense Induction and Disambiguation Rivaling Supervised Methods

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context and successful approaches are known to benefit many applications in Natural Language Processing. Although, supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words...

Semi-supervised induction of a concatenative morphology with simple morphotactics A model in the Morfessor family

Semi-supervised Induction with Basis Functions

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...

Semi-supervised Learning with Induced Word Senses for State of the Art Word Sense Disambiguation

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context, and successful approaches are known to benefit many applications in Natural Language Processing. Although supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words...

On Statistical Parsing of French with Supervised and Semi-Supervised Strategies

This paper reports results on grammatical induction for French. We investigate how to best train a parser on the French Treebank (Abeillé et al., 2003), viewing the task as a trade-off between generalizability and interpretability. We compare, for French, a supervised lexicalized parsing algorithm with a semi-supervised unlexicalized algorithm (Petrov et al., 2006) along the lines of (Crabbé an...

Semi-Supervised Semantic Role Labeling: Approaching from an Unsupervised Perspective

Reducing the reliance of semantic role labeling (SRL) methods on human-annotated data has become an active area of research. However, the prior work has largely focused on either (1) looking into ways to improve supervised SRL systems by producing surrogate annotated data and reducing sparsity of lexical features or (2) considering completely unsupervised semantic role induction settings. In th...