You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We describe a stochastic approach to partial parsing, i.e., the recognition of syntactic structures of limited depth. The technique utilises Markov Models, but goes beyond usual bracketing approaches, since it is capable of recognising not only the boundaries, but also the internal structure and syntactic category of simple as well as complex NP's, PP's, AP's and adverbials. We compare tagging accuracy for different applications and encoding schemes.

Cited Abstract(s):
[1]: This paper presents a statistical parser for natural language that obtains a parsing accuracy---roughly 87 precision and 86 recall---which surpasses the best previously published results on the Wall St. Journal domain. The parser itself requires very little human intervention, since the information it uses to make parsing decisions is specified in a concise and simple manner, and is combined in a fully automatic way under the maximum entropy framework. The observed running time of the parser on a test sentence is linear with respect to the sentence length. Furthermore, the parser returns several scored parses for a sentence, and this paper shows that a scheme to pick the best parse from the 20 highest scoring parses could yield a dramatically higher accuracy of 93 precision and recall.
In section , we sketched the simple bracketing technique described by , which provided motivation for our chunking method. As far as other approaches are concerned, our work is most closely related to that of , who use Markov Models in a preprocessing step to reduce the number of tree segments (called supertags ) that can be assigned to a word in a lexicalised Tree Adjoining Grammar. This approach makes parsing more efficient, but it needs a large training corpus, has to fight a large amount of ambiguity and needs a subsequent parsing step (also see [1] for the use of explanation-based learning for this purpose).