Recent research developments have allowed for efficient and robust deep parsing of real-world texts (Miyao and Tsujii, 2008).
The CR systems employed adopt a range of techniques but tend to avoid deep parsing and make use of a range of shallow parsing (e.g. for named entity recognition and part of speech tagging) and pattern-based techniques, supplemented with restrictions and inferences on HPO.
Their approach is based on the application of rules and transformations in the deep parse tree of the sentences for context decomposition and tuple extraction.
Towards the end of the methods section, the paper describes the use of "deep parsing".
COMMENT: "Towards the end of the methods section, the paper describes the use of "deep parsing".
"To find everything, you need to use semantic technology able to do deep parsing and look at families of words and synonyms," which Attensity's software has, she adds.
This can further be complicated by challenges with the implementation of relevant information extraction, also known as deep parsing, which is built on formal mathematical models.
Furthermore, the efficiency of dependency parsing compares favorably to phrase structure parsing or deep parsing.
We attempt to shed some light on these matters by performing a comparative evaluation of state-of-the-art syntactic parsers based on different frameworks: dependency parsing, phrase structure parsing and deep parsing.
Deep parsing follows the shortest dependency path hypothesis.
