CSCI 5832 Natural Language Processing Lecture 11 Jim Martin 01 14 19 CSCI 5832 Spring 2007 1 Today 2 22 More on CFGs and English grammar facts Break Parsing with CFGs Project discussions 01 14 19 CSCI 5832 Spring 2007 2 Context Free Grammars Capture constituency and ordering Ordering is easy well not really What are the rules that govern the ordering of words and bigger units in the language What s constituency How words group into units 01 14 19 CSCI 5832 Spring 2007 3 CFG Examples S NP VP NP Det NOMINAL NOMINAL Noun VP Verb Det a Noun flight Verb left 01 14 19 CSCI 5832 Spring 2007 4 Problems for CFGs Agreement Subcategorization Movement 01 14 19 CSCI 5832 Spring 2007 5 Agreement This dog Those dogs This dogs Those dog This dog eats Those dogs eat This dog eat Those dogs eats 01 14 19 CSCI 5832 Spring 2007 6 Agreement In English subjects and verbs have to agree in person and number Determiners and nouns have to agree in number Many languages have agreement systems that are far more complex than this 01 14 19 CSCI 5832 Spring 2007 7 Possible CFG Solution S NP VP NP Det Nominal VP V NP 01 14 19 SgS SgNP SgVP PlS PlNp PlVP SgNP SgDet SgNom PlNP PlDet PlNom PlVP PlV NP SgVP SgV Np CSCI 5832 Spring 2007 8 CFG Solution for Agreement It works and stays within the power of CFGs But its ugly And it doesn t scale all that well 01 14 19 CSCI 5832 Spring 2007 9 Subcategorization Sneeze John sneezed Find Please find a flight to NY NP Give Give me NP a cheaper fare NP Help Can you help me NP with a flight PP Prefer I prefer to leave earlier TO VP Told I was told United has a flight S 01 14 19 CSCI 5832 Spring 2007 10 Forward Pointer It turns out that verb subcategorization facts will provide a key element for semantic analysis determining who did what to who in an event 01 14 19 CSCI 5832 Spring 2007 11 Subcategorization John sneezed the book I prefer United has a flight Give with a flight Subcat expresses the constraints that a predicate verb for now places on the number and type of the argument it wants to take 01 14 19 CSCI 5832 Spring 2007 12 So So the various rules for VPs overgenerate They permit the presence of strings containing verbs and arguments that don t go together For example VP V NP therefore Sneezed the book is a VP since sneeze is a verb and the book is a valid NP 01 14 19 CSCI 5832 Spring 2007 13 Possible CFG Solution VP V VP V NP VP V NP PP 01 14 19 VP IntransV VP TransV NP VP TransPP NP PP CSCI 5832 Spring 2007 14 Movement Core canonical example My travel agent booked the flight 01 14 19 CSCI 5832 Spring 2007 15 Movement Core example My travel agent NP booked the flight NP VP S I e book is a straightforward transitive verb It expects a single NP arg within the VP as an argument and a single NP arg as the subject 01 14 19 CSCI 5832 Spring 2007 16 Movement What about Which flight do you want me to have the travel agent book The direct object argument to book isn t appearing in the right place It is in fact a long way from where its supposed to appear And note that its separated from its verb by 2 other verbs 01 14 19 CSCI 5832 Spring 2007 17 The Point CFGs appear to be just about what we need to account for a lot of basic syntactic structure in English But there are problems That can be dealt with adequately although not elegantly by staying within the CFG framework There are simpler more elegant solutions that take us out of the CFG framework beyond its formal power 01 14 19 CSCI 5832 Spring 2007 18 Break 01 14 19 CSCI 5832 Spring 2007 19 Break Quiz Average was X 1 True or False 2 Distributional and Morphological Evidence 3 aaa b or aa b or aa ab 4 They have a non zero intersection The machine in 4 accepts some of strings in L1 but not all It accepts some strings not in L1 as well That is with respect to L1 it makes two kinds of errors false positives and false negatives 01 14 19 CSCI 5832 Spring 2007 20 Break 5 aaaabbb a b 01 14 19 a b a 3 1 b 0 2 a b a 3 2 b 1 3 CSCI 5832 Spring 2007 21 Quiz c c 1 N c 1 Nc c 1 1 1 N 2 N 1 2 1 1 2 01 14 19 CSCI 5832 Spring 2007 22 Colloquium Today s CS colloquium talk is on NLP That s at 3 30 in ECCR 265 There s also an interesting talk tomorrow at noon in Muenzinger E214 on issues related to ML NLP 01 14 19 CSCI 5832 Spring 2007 23 Parsing Parsing with CFGs refers to the task of assigning correct trees to input strings Correct here means a tree that covers all and only the elements of the input and has an S at the top It doesn t actually mean that the system can select the correct tree from among all the possible trees 01 14 19 CSCI 5832 Spring 2007 24 Parsing As with everything of interest parsing involves a search which involves the making of choices We ll start with some basic meaning bad methods before moving on to the one or two that you need to know 01 14 19 CSCI 5832 Spring 2007 25 For Now Assume You have all the words already in some buffer The input isn t POS tagged We won t worry about morphological analysis All the words are known 01 14 19 CSCI 5832 Spring 2007 26 Top Down Parsing Since we re trying to find trees rooted with an S Sentences start with the rules that give us an S Then work your way down from there to the words 01 14 19 CSCI 5832 Spring 2007 27 Top Down Space 01 14 19 CSCI 5832 Spring 2007 28 Bottom Up Parsing Of course we also want trees that cover the input words So start with trees that link up with the words in the right way Then work your way up from there 01 14 19 CSCI 5832 Spring 2007 29 Bottom Up Space 01 14 19 CSCI 5832 Spring 2007 30 Bottom Up Space 01 14 19 CSCI 5832 Spring 2007 31 Control Of course in both cases we left out how to keep track of the search space and how to make choices Which node to try to expand next Which grammar rule to use to expand a node 01 14 19 CSCI 5832 Spring 2007 32 Top Down and Bottom Up Top down Only searches for trees that can be answers i e S s But also suggests …
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