Bootstrapping

This paper describes a "bootstrapping" method which uses a broad-coverage, rule-based parser to compute probabilities while parsing an untagged corpus of NL text, and which then incorporates those probabilities into …...

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 …...

A wide range of supervised learning algorithms has been applied to Text Categorization. However, the supervised learning approaches have some problems. One of them is that they require a large, …...

Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In this paper, we …...

We investigate the unsupervised detection of semi-fixed cue phrases such as “This paper proposes a novel approach. . . 1” from unseen text, on the basis of only a handful …...

We describe our submission to the domain adaptation track of the CoNLL07 shared task in the open class for systems using ex- ternal resources. Our main finding was that it …...

Bootstrapping has a tendency, called seman- tic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of bootstrap- ping has the …...

Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retraining the …...

This paper presents a new approach to selecting the initial seed set using stratified sampling strategy in bootstrapping-based semi-supervised learning for semantic relation classification. First, the training data is partitioned …...

Open-class semantic lexicon induction is of great interest for current knowledge harvest- ing algorithms. We propose a general frame- work that uses patterns in bootstrapping fash- ion to learn open-class …...

We present a named entity recognition (NER) system for extracting product attributes and values from listing titles. Information extrac- tion from short listing titles present a unique challenge, with the …...

Responding to the need for semantic lexical resources in natural language processing ap- plications, we examine methods to acquire noun compounds (NCs), e.g., orange juice, to- gether with suitable fine-grained …...

Most event extraction systems are trained with supervised learning and rely on a col- lection of annotated documents. Due to the domain-specificity of this task, event extraction systems must be …...

We describe our submission to SemEval-2016 Task 3 on Community Question Answering. We participated in subtask A, which asks to rerank the comments from the thread for a given forum …...

AbstractText generation may require the pluralization of nouns, such as in context-sensitive user interfaces and in natural language generation more broadly. While this has been solved for the widely-used languages, …...

AbstractThe quality of a summarization evaluation metric is quantified by calculating the correlation between its scores and human annotations across a large number of summaries. Currently, it is unclear how …...

AbstractAimed at generating a seed lexicon for use in downstream natural language tasks and unsupervised methods for bilingual lexicon induction have received much attention in the academic literature recently. While …...

AbstractBootstrapping has become the mainstream method for entity set expansion. Conventional bootstrapping methods mostly define the expansion boundary using seed-based distance metrics, which heavily depend on the quality of selected …...