Asli Celikyilmaz, Marcus Thint, and Zhiheng Huang. 2009. A Graph-based Semi-Supervised Learning for Question-Answering. In Proceedings of the Joint Conference ...
We implement a semi-supervised learning (SSL) approach to demonstrate that utilization of more unlabeled data points can improve the answer-ranking task of QA.
We investigate a graph-based semi-supervised learning approach for labeling semantic com- ponents of questions such as topic, focus,.
We implement a semi-supervised learning (SSL) approach to demonstrate that utilization of more unlabeled data points can improve the answer-ranking task of QA.
We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Us- ing textual entailment analysis, ...
With the new graph representation, this work investigates a graph-based semi-supervised learning approach for labeling semantic components of questions such ...
We implement a semi-supervised learning (SSL) approach to demonstrate that utilization of more unlabeled data points can improve the answer-ranking task of QA.
This repository contains graph-based semi-supervised learning (GSSL) papers mentioned in our GSSL survey. We will update this paper list to include new GSSL ...
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Graph-based SSL for QA. A Graph-based Semi-Supervised. Learning for Question-Answering ... Graph-based SSL for QA ... the Graph-based Semi-Supervised learning. 4 / ...
We present a series of novel semi-supervised learning approaches arising from a graph representation, where labeled and unlabeled instances are represented as.