Contextually-related Entities - Stanford University上下文相关的实体-斯坦福大学.pptVIP

Linguistics 187: Grammar Engineering Ron Kaplan, Tracy King, Martin Forst Administrivia Schedule: Office hours Requirements Overview Applications of Language Engineering Grammar engineering for deep processing Draws on theoretical linguistics, software engineering Theoretical linguistics = papers Generalizations, universality, idealization (competence) Software engineering = programs Coverage, interface, QA, maintainability, efficiency, practicality Grammar engineering Grammar::Theory = Program::Programming language Reflect linguistic generalizations Respect special cases of ordinary language Deal with large-scale interactions Theory/practice trade-offs What is a shallow grammar often trained automatically from marked up corpora part of speech tagging chunking trees POS tagging and Chunking Part of speech tagging: I/PRP saw/VBD her/PRP duck/VB ./PUNCT I/PRP saw/VBD her/PRP$ duck/NN ./PUNCT Chunking: general chunking [I begin] [with an intuition]: [when I read] [a sentence], [I read it] [a chunk] [at a time]. (Abney) NP chunking [NP President Obama] visited [NP the Hermitage] in [NP Leningrad] Treebank grammars Phrase structure tree (c-structure) Annotations for heads, grammatical functions Deep grammars Provide detailed syntactic/semantic analyses LFG (ParGram), HPSG (LinGO, Matrix) Grammatical functions, tense, number, etc. Mary wants to leave. subj(want~1,Mary~3) comp(want~1,leave~2) subj(leave~2,Mary~3) tense(want~1,present) Usually manually constructed linguistically motivated rules Why would you want one Meaning sensitive applications overkill for many NLP applications, crucial for others Applications which use shallow methods for English may not work for free word order languages can read many functions off of trees in English SUBJ: NP sister to VP [S [NP Mary] [VP left]] OBJ: first NP sister to V [S [NP Mary] [VP saw [NP John]]] need other information in German, Japanese, etc. Deep analysis matters… if you care about