In this thesis, we compare the description length of different grammars, and extend the research of automatic grammar learning to the grammar production of Stanford parser. In our research before, we have introduced that how to minimize the description length of the grammar which is generated from the Academia Sinica Balanced Corpus. Based on the concept of data compression, the encoding method in our research is effective in reducing the description length of a text corpus. Moreover, we further discussed about the description length of two special cases of context-free grammars: exhaustive and recursive. The exhaustive grammar is that for every distinct sentence in the corpus is derived, and the recursive one covers all strings. In our research of this thesis, we use a parsing tool called "Stanford parser" to parse sentences and generate grammar rules. We also compare the description length of the grammar parsed by machine with the grammar fixed by artificial. In one of the experiments, we use Stanford parser to parse ASBC corpus, and the description length is 53.0Mb. The description length of rule is only 52,683. In the other experiment, we use Stanford parser to parse Sinica Treebank and compare the description length of the generated grammar with the origin. The result shows that the description length of grammar of the Sinica Treebank is 2.76Mb, and the grammar generated by Stanford parser is 4.02Mb.

This thesis deals with knowledge discovery from social media. This thesis is focused on feature based opinion mining from user reviews. In theoretical part were described methods of opinion mining and natural language processing. Main parts of this thesis were design and implementation of library for opinion mining based on Stanford Parser and lexicon WordNet. For feature identi cation was used dependency grammar, implicit features were mined with method CoAR and opinions were classi ed with supervised algorithm. Finally were given experiments with implemented library and examples of usage.