Learning dictionaries for information extraction by multi-level bootstrapping
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Automatic retrieval and clustering of similar words
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Fine grained classification of named entities
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Espresso: leveraging generic patterns for automatically harvesting semantic relations
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KnowItNow: fast, scalable information extraction from the web
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Integrating pattern-based and distributional similarity methods for lexical entailment acquisition
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Weakly-supervised discovery of named entities using web search queries
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Unsupervised query segmentation using generative language models and wikipedia
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Context-aware query suggestion by mining click-through and session data
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Exploiting web search to generate synonyms for entities
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Understanding user's query intent with wikipedia
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Weakly-supervised acquisition of labeled class instances using graph random walks
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Web-scale distributional similarity and entity set expansion
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A web service for automatic word class acquisition
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Coupled semi-supervised learning for information extraction
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Understanding the semantic structure of noun phrase queries
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Open entity extraction from web search query logs
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Materializing multi-relational databases from the web using taxonomic queries
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Ranking class labels using query sessions
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Recovering semantics of tables on the web
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Automatically building training examples for entity extraction
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The role of query sessions in extracting instance attributes from web search queries
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Ensemble semantics for large-scale unsupervised relation extraction
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Wikipedia entity expansion and attribute extraction from the web using semi-supervised learning
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Acquisition of open-domain classes via intersective semantics
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Combining information extraction systems yields significantly higher quality resources than each system in isolation. In this paper, we generalize such a mixing of sources and features in a framework called Ensemble Semantics. We show very large gains in entity extraction by combining state-of-the-art distributional and pattern-based systems with a large set of features from a webcrawl, query logs, and Wikipedia. Experimental results on a web-scale extraction of actors, athletes and musicians show significantly higher mean average precision scores (29% gain) compared with the current state of the art.