Weakly-Supervised Opinion Summarization by Leveraging External Information

Weakly Supervised Summarization of Web Videos (Supplementary Material)

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WikiSent: Weakly Supervised Sentiment Analysis through Extractive Summarization with Wikipedia

This paper describes a weakly supervised system for sentiment analysis in the movie review domain. The objective is to classify a movie review into a polarity class, positive or negative, based on those sentences bearing opinion on the movie alone. The irrelevant text, not directly related to the reviewer opinion on the movie, is left out of analysis. Wikipedia incorporates the world knowledge ...

Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter

Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse...

Opinion Extraction and Summarization

An Approach for Weakly-Supervised Deep Information Retrieval

Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an aŠempt to €ll this gap, we present an approach that—given a weak training set of pseudo-queries, documents, relevance information—€lters the data to produce e‚ective po...