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  • × author_ss:"Chandrasekar, R."
  • × theme_ss:"Computerlinguistik"
  1. Chandrasekar, R.; Bangalore, S.: Glean : using syntactic information in document filtering (2002) 0.00 
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    Abstract
    In today's networked world, a huge amount of data is available in machine-processable form. Likewise, there are any number of search engines and specialized information retrieval (IR) programs that seek to extract relevant information from these data repositories. Most IR systems and Web search engines have been designed for speed and tend to maximize the quantity of information (recall) rather than the relevance of the information (precision) to the query. As a result, search engine users get inundated with information for practically any query, and are forced to scan a large number of potentially relevant items to get to the information of interest. The Holy Grail of IR is to somehow retrieve those and only those documents pertinent to the user's query. Polysemy and synonymy - the fact that often there are several meanings for a word or phrase, and likewise, many ways to express a conceptmake this a very hard task. While conventional IR systems provide usable solutions, there are a number of open problems to be solved, in areas such as syntactic processing, semantic analysis, and user modeling, before we develop systems that "understand" user queries and text collections. Meanwhile, we can use tools and techniques available today to improve the precision of retrieval. In particular, using the approach described in this article, we can approximate understanding using the syntactic structure and patterns of language use that is latent in documents to make IR more effective.
    Type
    a
  2. Chandrasekar, R.; Srinivas, B.: Automatic induction of rules for text simplification (1997) 0.00 
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    Abstract
    Explores methods to automatically transform sentences in order to make them simpler. These methods involve the use of a rule-based system, driven by the syntax of the text in the domain of interest. Hand-crafting rules for every domain is time-consuming and impractical. Describes an algorithm and an implementation by which generalized rules for simplification are automatically induced from annotated training materials using a novel partial parsing technique, which combines constituent structure and dependency information. The algorithm employs example-based generalisations on linguistically motivated structures
    Type
    a

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