Digital Text and Data Processing Week 4

□ Making computers understand languages spoken by human beings □ Applications: □ Part of Speech Tagging □ Sentiment analysis □ Information extraction □ Machine translation □ Summarising □ Paraphrasing Natual Language Processing

providing the syntactical category or words within in a sentence: Part of speech tagging “The Signora had no business to do it," said Miss Bartlett, "no business at all. The/DT Signora/NNP had/VBD no/DT business/NN to/TO do/VB it/PRP said/VBD Miss/NNP Bartlett/NNP no/DT business/NN at/IN all/DT

□ Combination of a lexicon-based and a rule- based approach □ A lexicon entry looks as follows: Talk VB NN □ Initial Results are improved with transformation rules: e.g. VB NN PREVIOUSTAG JJ she could re-enter the world of rapid/JJ talk/VB, which was alone familiar to her So she did want to talk/VB about her broken engagement Brill’s POS tagger

□ Lingua::EN::Tagger (a “trained” POS Tagger) □ Lingua::EN::Fathom (Readability measures) □ Lingua::EN::Sentence □ Also: □ LINGua::DE □ LINGua::FR □ LINGua::ES □ LINGua::Klingon PERL NLP modules

Morphadorner