The Stanford NLP (Natural Language Processing) Group About | Citing | Questions | Download | Included Tools | Extensions | Release history | Sample output | Online | FAQ A natural language parser is a program that works out the grammatical structure of sentences, for instance, which groups of words go together (as "phrases") and which words are the subject or object of a verb. Probabilistic parsers use knowledge of language gained from hand-parsed sentences to try to produce the most likely analysis of new sentences. These statistical parsers still make some mistakes, but commonly work rather well. Their development was one of the biggest breakthroughs in natural language processing in the 1990s. You can try out our parser online.
Retail + Social + Mobile = @WalmartLabs Eric Schmidt famously observed that every two days now, we create as much data as we did from the dawn of civilization until 2003. A lot of the new data is not locked away in enterprise databases, but is freely available to the world in the form of social media: status updates, tweets, blogs, and videos. At Kosmix, we’ve been building a platform, called the Social Genome, to organize this data deluge by adding a layer of semantic understanding. Conversations in social media revolve around “social elements” such as people, places, topics, products, and events.
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Parsing Within computational linguistics the term is used to refer to the formal analysis by a computer of a sentence or other string of words into its constituents, resulting in a parse tree showing their syntactic relation to each other, which may also contain semantic and other information. The term is also used in psycholinguistics when describing language comprehension. In this context, parsing refers to the way that human beings analyze a sentence or phrase (in spoken language or text) "in terms of grammatical constituents, identifying the parts of speech, syntactic relations, etc."  This term is especially common when discussing what linguistic cues help speakers to interpret garden-path sentences. Human languages
Cluster Execution Compute clusters often run idle because of a lack of applications that can be run in the cluster environment and the enormous effort required to operate, maintain, and support applications on the grid. KNIME Cluster Execution tackles this problem by providing a thin connection layer between KNIME and the cluster, which allows every node running in KNIME and every application integrated in KNIME to be executed on the cluster. Submission of data to the cluster and collection of the results is made very simple. Long-running analysis workflows can be executed on the compute cluster, thus releasing local resources for other productive work.