Benjamin Rozenfeld

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The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the(More)
Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional Information Extraction methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while(More)
The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates(More)
Many errors produced by unsupervised and semi-supervised relation extraction (RE) systems occur because of wrong recognition of entities that participate in the relations. This is especially true for systems that do not use separate named-entity recognition components, instead relying on general-purpose shallow parsing. Such systems have greater(More)
The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domainand relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the(More)
Recent studies suggest various approaches to the problem of analyzing sentiment attributed to stocks cited in news sites and blogs across the web. These approaches, though they may differ in their specific choice of implementation, are generally based on dictionary and phrase level Sentiment Analysis (SA) compounded by simple Machine Learning strategies.(More)
A key question in sentiment analysis is whether sentiment ex-pressions, in a given text, are related to particular entities. This is an imperative question, since people are typically interested in sentiments on specific entities and not in the overall sentiment articulated in an article or a document. Sentiment relevance is aimed at addressing this precise(More)
382 Background: Despite the development of treatment guidelines, the management of renal cell carcinoma (RCC) remains overly subjective. Nomograms and statistical algorithms promise more objective decision making by patients and physicians by quantifying risk. Unfortunately, these tools remain underutilized in daily clinical practice because they are both(More)
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