Co-Training

We propose a novel Co-Training method for statistical parsing. The algorithm takes as input a small corpus (9695 sentences) annotated with parse trees, a dictionary of possible lexicalized structures for …...

This paper investigates the use of a language inde- pendent model for named entity recognition based on iterative learning in a co-training fashion, using word-internal and contextual information as inde- …...

This paper presents a new bootstrapping approach to named entity (NE) classification. This approach only requires a few common noun/pronoun seeds that correspond to the concept for the target NE …...

We present a practical co-training method for bootstrapping statistical parsers using a small amount of manu- ally parsed training material and a much larger pool of raw sentences. Experi- mental …...

This paper investigates bootstrapping for statis- tical parsers to reduce their reliance on manu- ally annotated training data. We consider both a mostly-unsupervised approach, co-training, in which two parsers are …...

This paper proposes the use of uncertainty reduction in machine learning methods such as co-training and bilingual boot- strapping, which are referred to, in a gen- eral term, as ‘collaborative …...

This paper investigates the application of co- training and self-training to word sense disam- biguation. Optimal and empirical parameter se- lection methods for co-training and self-training are investigated, with various …...

It is difficult to identify sentence impor- tance from a single point of view. In this paper, we propose a learning-based ap- proach to combine various sentence fea- tures. They …...

Martins et al. (2008) presented what to the best of our knowledge still ranks as the best overall result on the CONLL- X Shared Task datasets. The paper shows how …...

People tweet more than 100 Million times daily, yielding a noisy, informal, but some- times informative corpus of 140-character messages that mirrors the zeitgeist in an un- precedented manner. The …...

Resolving coordination ambiguity is a clas- sic hard problem. This paper looks at co- ordination disambiguation in complex noun phrases (NPs). Parsers trained on the Penn Treebank are reporting impressive …...

We describe an automatic paraphrase-inference procedure for a highly inflected language like Arabic. Paraphrases are derived from comparable documents, that is, distinct documents dealing with the same topic. A co-training …...

Our system breaks down the problem of rank- ing a list of lexical substitutions according to how simple they are in a given context into a series of pairwise comparisons …...

We investigate the task of medical concept coreference resolution in clinical text using two semi-supervised methods, co-training and multi-view learning with posterior regulariza- tion. By extracting semantic and temporal features …...

In this paper we report an empirical study on semi-supervised Chinese word segmenta- tion using co-training. We utilize two seg- menters: 1) a word-based segmenter lever- aging a word-level language …...

We compare two different methods in domain adaptation applied to constituent parsing: parser combination and co- training, each used to transfer information from the source domain of news to the …...

AbstractDiscourse parsing is a popular technique widely used in text understanding, sentiment analysis and other NLP tasks. However, for most discourse parsers, the performance varies significantly across different discourse relations. …...

AbstractCo-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted labels on the unlabeled …...

AbstractUser-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be …...

AbstractDiscourse parsing has been studied for decades. However, it still remains challenging to utilize discourse parsing for real-world applications because the parsing accuracy degrades significantly on out-of-domain text. In this …...