Syntactic Parsing. Prof. Sameer Singh CS 295: STATISTICAL NLP WINTER February 7, 2017
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1 Syntactic Parsing Prof. Sameer Singh CS 295: STATISTICAL NLP WINTER 2017 February 7, 2017 Based on slides from Nathan Schneider, Noah Smith, Marine Carpuat, Dan Jurafsky, and everyone else they copied from.
2 Outline Syntactic Parsing Context Free Grammars Parsing: CKY Algorithm CS 295: STATISTICAL NLP (WINTER 2017) 2
3 Outline Syntactic Parsing Context Free Grammars Parsing: CKY Algorithm CS 295: STATISTICAL NLP (WINTER 2017) 3
4 Limitations of Sequence Tags John Smith shot Bill in his pajamas. What happened? Who shot who? Who was wearing the pajamas? Using CS 295: STATISTICAL NLP (WINTER 2017) 4
5 Constituents Constituent behave as a unit that can be rearranged: John talked [to the children] [about drugs]. John talked [about drugs] [to the children]. John talked drugs to the children about Or substituted/expanded: John talked [to the children taking the drugs] [about alcohol]. Harry the Horse a high-class spot such as Mindy s the Broadway coppers the reason he comes into the Hot Box they three parties from Brooklyn X arrive(s) attract(s) love(s) sit(s) Noun phrases appear before verbs in English. CS 295: STATISTICAL NLP (WINTER 2017) 5
6 Constituents and Grammars Grammar Tells you how the constituents can be arranged Implicit knowledge for us (we often can t tell why something is wrong) Generate all, and only, the possible sentences of the language Different from meaning: Colorless green ideas sleep furiously. The words are in the right order, And that ideas are green and colorless, And that ideas sleep, And that sleeping is done furiously, As opposed to: sleep green furiously ideas colorless CS 295: STATISTICAL NLP (WINTER 2017) 6
7 Uses of Parsing [ send [the text message from James] [to Sharon] ] [ translate [the message] [from Hindi] [to English] ] Grammar checkers Dialog systems High precision question answering Named entity recognition Sentence compression Extracting opinions about products Improved interaction in computer games Helping linguists find data Machine translation Relation extraction systems CS 295: STATISTICAL NLP (WINTER 2017) 7
8 Outline Syntactic Parsing Context Free Grammars Parsing: CKY Algorithm CS 295: STATISTICAL NLP (WINTER 2017) 8
9 Basic Grammar: Regular Expr. You can capture individual words: (man dog cat) Simple sentences: (man dog cat)(ate loves consumed)(. food lunch) Infinite length? Yes! men (who like (cats dogs))* cry. Finite State men Machine Start S1 who dogs S2 cats like S3 End cry But too weak for English. CS 295: STATISTICAL NLP (WINTER 2017) 9
10 Context-Free Grammars Grammar, G Terminal Symbols Non-terminal Symbols Rules Grammar applies rules recursively.. If we can construct the input sentence, it is in the grammar, otherwise not. CS 295: STATISTICAL NLP (WINTER 2017) 10
11 Example CFG CS 295: STATISTICAL NLP (WINTER 2017) 11
12 Example Parse Tree I prefer a morning flight. CS 295: STATISTICAL NLP (WINTER 2017) 12
13 Example Parse Tree: Brackets I prefer a morning flight. CS 295: STATISTICAL NLP (WINTER 2017) 13
14 More details: Noun Phrases Simple Noun Phrases NP ProperNoun NP Det Nominal Nominal Noun Noun Nominal Complex Noun Phrases all the morning flights from Denver to Tampa leaving before 10 CS 295: STATISTICAL NLP (WINTER 2017) 14
15 Recursive Noun Phrases this is the house this is the house that Jack built this is the cat that lives in the house that Jack built this is the dog that chased the cat that lives in the house that Jack built this is the flea that bit the dog that chased the cat that lives in the house the Jack built this is the virus that infected the flea that bit the dog that chased the cat that lives in the house that Jack built CS 295: STATISTICAL NLP (WINTER 2017) 15
16 More details: Verb Phrases Simple Verb Phrases VP Verb VP Verb NP VP Verb NP PP VP Verb PP disappear prefer a morning flight leave Boston in the morning leave in the morning But all verbs are not the same! (this grammar overgenerates) Solution: subcategorize! Sneezed: John sneezed. Find: Please find a flight to NY. Give: Give me a cheaper fare. Help: Can you help me with a flight? Prefer: I prefer to leave earlier. Told: I was told United has a flight. CS 295: STATISTICAL NLP (WINTER 2017) 16
17 Types of Sentences Declarative S NP VP A plane left. Imperative S VP Show the plane. Yes/no Questions S Aux NP VP Did the plane leave? Wh-Questions S WhNP Aux NP VP When did the plane leave? CS 295: STATISTICAL NLP (WINTER 2017) 17
18 Source of Grammar? Manual Write symbolic grammar (CFG or often richer) and lexicon S NP VP NN interest NP (DT) NN NNS rates NP NN NNS NNS raises NP NNP VBP interest VP V NP VBZ rates Used grammar/proof systems to prove parses from words Noam Chomsky Fed raises interest rates 0.5% in effort to control inflation Minimal grammar: 36 parses Simple 10 rule grammar: 592 parses Real-size broad-coverage grammar: millions of parses CS 295: STATISTICAL NLP (WINTER 2017) 18
19 Source of Grammar? From data! The Penn Treebank Building a treebank seems a lot slower and less useful than building a grammar But a treebank gives us many things Reusability of the labor Many parsers, POS taggers, etc. Valuable resource for linguistics Broad coverage Frequencies and distributional information A way to evaluate systems [Marcus et al. 1993, Computational Linguistics] CS 295: STATISTICAL NLP (WINTER 2017) 19
20 ( (S (NP-SBJ (DT The) (NN move)) (VP (VBD followed) (NP (NP (DT a) (NN round)) (PP (IN of) (NP (NP (JJ similar) (NNS increases)) (PP (IN by) (NP (JJ other) (NNS lenders))) (PP (IN against) (NP (NNP Arizona) (JJ real) (NN estate) (NNS loans)))))) (,,) (S-ADV (NP-SBJ (-NONE- *)) (VP (VBG reflecting) (NP (NP (DT a) (VBG continuing) (NN decline)) (PP-LOC (IN in) (NP (DT that) (NN market))))))) (..))) CS 295: STATISTICAL NLP (WINTER 2017) 20
21 Some of the rules, with counts PP IN NP S NP-SBJ VP NP-SBJ -NONE NP NP PP NP DT NN S NP-SBJ VP VP TO VP PP-LOC IN NP NP-SBJ PRP NP -NONE NP NN VP VBG S 985 NP-SBJ NN 983 PP-MNR IN NP 983 NP-SBJ DT 969 VP VBN VP 100 VP VBD PP-PRD 100 PRN : NP : 100 NP DT JJS 100 NP-CLR NN 99 NP-SBJ-1 DT NNP 98 VP VBN NP PP-DIR 98 VP VBD PP-TMP 98 PP-TMP VBG NP 97 VP VBD ADVP-TMP VP WHNP-1 WRB JJ 10 VP VP CC VP PP-TMP 10 VP VP CC VP ADVP-MNR 10 VP VBZ S, SBAR-ADV 10 VP VBZ S ADVP-TMP 4500 rules for VP! CS 295: STATISTICAL NLP (WINTER 2017) 21
22 Evaluating Parses Each parse tree is represented by a list of tuples: Use this to estimate precision/recall! CS 295: STATISTICAL NLP (WINTER 2017) 22
23 Evaluating Parses: Example CS 295: STATISTICAL NLP (WINTER 2017) 23
24 Outline Syntactic Parsing Context Free Grammars Parsing: CKY Algorithm CS 295: STATISTICAL NLP (WINTER 2017) 24
25 The Parsing Problem Given sentence x and grammar G, Recognition Is sentence x in the grammar? If so, prove it. Proof is a deduction, valid parse tree. Parsing Show one or more derivations for x in G. Even with small grammars, brute force grows exponentially! Book that flight CS 295: STATISTICAL NLP (WINTER 2017) 25
26 Top Down Parsing Considers only valid trees But are inconsistent with the words! Book that flight CS 295: STATISTICAL NLP (WINTER 2017) 26
27 Bottom-up Parsing Book that flight Builds only consistent trees But most of them are invalid (don t go anywhere)! CS 295: STATISTICAL NLP (WINTER 2017) 27
28 Chomsky Normal Form Context free grammar where all non-terminals to go: - 2 non-terminals, or - A single terminal A B C D w Converting to CNF Case 1 Case 2 A B B C D B w A C D A w A B C D E A X E X Y D Y B C CS 295: STATISTICAL NLP (WINTER 2017) 28
29 Original Grammar Chomsky Normal Form CS 295: STATISTICAL NLP (WINTER 2017) 29
30 Dynamic Programming table[i,j] = Set of all valid non-terminals for the constituent span (i,j) Base case Rule: A word[j] A should be in table[j-1,j] A word[j] (j-1,j) Recursion Rule: A B C (i,j) A If you find a k such that B is in table[i,k], and C is in table[k,j], then A should be in table[i,j] B (i,k) C (k,j) CS 295: STATISTICAL NLP (WINTER 2017) 30
31 CKY Algorithm Book the flight through TWA CS 295: STATISTICAL NLP (WINTER 2017) 31
32 Outline Syntactic Parsing Context Free Grammars Parsing: CKY Algorithm CS 295: STATISTICAL NLP (WINTER 2017) 32
33 Upcoming Homework Homework 2 is due in a week: February 13, 2017 Homework 1 grades will be available tonight Project Proposal is due on tonight Only 2 pages Summaries Paper summaries: February 17, February 28, March 14 Only 1 page each CS 295: STATISTICAL NLP (WINTER 2017) 33
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