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In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often res ...
For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...
Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...
Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...
Google’s highly successful business model is based on selling words that appear in search queries. Organizing several million auctions per minute, the company has created the first global linguistic market and demonstrated that linguistic capitalism is a l ...
Recently, there has been a lot of effort to represent words in continuous vector spaces. Those representations have been shown to capture both semantic and syntactic information about words. However, distributed representations of phrases remain a challeng ...
This paper introduces a model of multiple-instance learning applied to the prediction of aspect ratings or judgments of specific properties of an item from user-contributed texts such as product reviews. Each variable-length text is represented by several ...
We present a descriptive analysis of Twitter data. Our study focuses on extracting the main side effects associated with HIV treatments. The crux of our work was the identification of personal tweets referring to HIV. We summarize our results in an infogra ...
Emoticons are widely used to express positive or negative sentiment on Twitter. We report on a study with live users to determine whether emoticons are used to merely emphasize the sentiment of tweets, or whether they are the main elements carrying the sen ...
This paper introduces a model of multiple-instance learning applied to the prediction of aspect ratings or judgments of specific properties of an item from user-contributed texts such as product reviews. Each variable-length text is represented by several ...
