Treebank Grammars and Parser Evaluation
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1 Treebank Grammars and Parser Evaluation Syntactic analysis (5LN455) Sara Stymne Department of Linguistics and Philology Based on slides from Marco Kuhlmann
2 Recap: Probabilistic parsing
3 Probabilistic context-free grammars A probabilistic context-free grammar (PCFG) is a context-free grammar where each rule r has been assigned a probability p(r) between 0 and 1 the probabilities of rules with the same left-hand side sum up to 1
4 Probability of a parse tree S 1/1 NP VP 1/3 8/9 Pro Verb NP 1/3 I booked Det Nom 1/3 a Nom 2/3 PP Noun from LA flight Probability: 16/729
5 Probability of a parse tree S 1/1 NP VP 1/3 1/9 Pro Verb NP 1/3 PP I booked Det Nom 2/3 from LA a Noun flight Probability: 6/729
6 Computing the most probable tree for each max from 2 to n for each min from max - 2 down to 0 for each syntactic category C double best = undefined for each binary rule C -> C1 C2 for each mid from min + 1 to max - 1 double t1 = chart[min][mid][c1] double t2 = chart[mid][max][c2] double candidate = t1 * t2 * p(c -> C1 C2) if candidate > best then best = candidate chart[min][max][c] = best
7 Backpointers double best = undefined Backpointer backpointer = undefined... if candidate > best then best = candidate // We found a better tree; update the backpointer! Backpointer bp1 = backpointerchart[min][mid][c1] Backpointer bp2 = backpointerchart[mid][max][c2] backpointer = new Backpointer(C -> C1 C2, bp1, bp2)... chart[min][max][c] = best backpointerchart[min][max][c] = backpointer
8 Treebank grammars
9 Treebank grammars Treebanks Treebanks are corpora in which each sentence has been annotated with a syntactic analysis. The annotation process requires detailed guidelines and measures for quality control. Producing a high-quality treebank is both time-consuming and expensive.
10 Treebank grammars The Penn Treebank One of the most widely known treebanks is the Penn TreeBank (PTB). The PTB was compiled at the University of Pennsylvania; the latest release was in Most well known is the Wall Street Journal section of the Penn Treebank. This section contains 1 million tokens from the Wall Street Journal ( ).
11 Treebank grammars The Penn Treebank ( (S (NP-SBJ (NP (NNP Pierre) (NNP Vinken) ) (,,) (ADJP (NP (CD 61) (NNS years) ) (JJ old) ) (,,) ) (VP (MD will) (VP (VB join) (NP (DT the) (NN board) ) (PP-CLR (IN as) (NP (DT a) (JJ nonexecutive) (NN director) )) (NP-TMP (NNP Nov.) (CD 29) ))) (..) ))
12 Treebank grammars PTB bracket labels Word Description Phrase Description NNP Proper noun S Declarative clause CD Cardinal number NP Noun phrase NNS Noun, plural ADJP Adjective phrase JJ Adjective VP Verb phrase MD Modal PP Prepositional VB Verb, base form ADVP Adverb phrase DT Determiner RRC Reduced relative NN Noun, singular WHNP Wh-noun phrase IN Preposition NAC Not a constituent
13 Treebank grammars Reading rules off the trees Given a treebank, we can construct a grammar by reading rules off the phrase structure trees. Sample grammar rule Span S NP-SBJ VP. Pierre Vinken Nov. 29. NP-SBJ NP, ADJP, VP MD VP NP DT NN Pierre Vinken, 61 years old, will join the board the board
14 Treebank grammars The Penn Treebank ( (S (NP-SBJ (NP (NNP Pierre) (NNP Vinken) ) (,,) (ADJP (NP (CD 61) (NNS years) ) (JJ old) ) (,,) ) (VP (MD will) (VP (VB join) (NP (DT the) (NN board) ) (PP-CLR (IN as) (NP (DT a) (JJ nonexecutive) (NN director) )) (NP-TMP (NNP Nov.) (CD 29) ))) (..) ))
15 Treebank grammars The Penn Treebank ( (S (NP-SBJ (NP (NNP Pierre) (NNP Vinken) ) (,,) (ADJP (NP (CD 61) (NNS years) ) (JJ old) ) (,,) ) (VP (MD will) (VP (VB join) (NP (DT the) (NN board) ) (PP-CLR (IN as) (NP (DT a) (JJ nonexecutive) (NN director) )) (NP-TMP (NNP Nov.) (CD 29) ))) (..) )) S NP-SBJ VP.
16 Treebank grammars The Penn Treebank ( (S (NP-SBJ (NP (NNP Pierre) (NNP Vinken) ) (,,) (ADJP (NP (CD 61) (NNS years) ) (JJ old) ) (,,) ) (VP (MD will) (VP (VB join) (NP (DT the) (NN board) ) (PP-CLR (IN as) (NP (DT a) (JJ nonexecutive) (NN director) )) (NP-TMP (NNP Nov.) (CD 29) ))) (..) )) NP-SBJ NP, ADJP,
17 Treebank grammars The Penn Treebank ( (S (NP-SBJ (NP (NNP Pierre) (NNP Vinken) ) (,,) (ADJP (NP (CD 61) (NNS years) ) (JJ old) ) (,,) ) (VP (MD will) (VP (VB join) (NP (DT the) (NN board) ) (PP-CLR (IN as) (NP (DT a) (JJ nonexecutive) (NN director) )) (NP-TMP (NNP Nov.) (CD 29) ))) (..) )) ADJP NP JJ
18 Treebank grammars The Penn Treebank ( (S (NP-SBJ (NP (NNP Pierre) (NNP Vinken) ) (,,) (ADJP (NP (CD 61) (NNS years) ) (JJ old) ) (,,) ) (VP (MD will) (VP (VB join) (NP (DT the) (NN board) ) (PP-CLR (IN as) (NP (DT a) (JJ nonexecutive) (NN director) )) (NP-TMP (NNP Nov.) (CD 29) ))) (..) )) NP CD NNS
19 Treebank grammars The Penn Treebank ( (S (NP-SBJ (NP (NNP Pierre) (NNP Vinken) ) (,,) (ADJP (NP (CD 61) (NNS years) ) (JJ old) ) (,,) ) (VP (MD will) (VP (VB join) (NP (DT the) (NN board) ) (PP-CLR (IN as) (NP (DT a) (JJ nonexecutive) (NN director) )) (NP-TMP (NNP Nov.) (CD 29) ))) (..) )) NP NNP NNP
20 Treebank grammars Coverage of treebank grammars A treebank grammar will account for all analyses in the treebank. It can also be used to derive sentences that were not observed in the treebank.
21 Treebank grammars Properties of treebank grammars Treebank grammars are typically rather flat. Annotators tend to avoid deeply nested structures. Grammar transformations. In order to be useful in practice, treebank grammars need to be transformed in various ways. Treebank grammars are large. The vanilla PTB grammar has 29,846 rules.
22 Treebank grammars Estimating rule probabilities The simplest way to obtain rule probabilities is relative frequency estimation. Step 1: Count the number of occurrences of each rule in the treebank. Step 2: Divide this number by the total number of rule occurrences for the same left-hand side. The grammar that you use in the assignment is produced in this way.
23 Parser evaluation
24 Parser evaluation Different types of evaluation Intrinsic versus extrinsic evaluation. Evaluate relative to some gold standard vs. evaluate in the context of some specific task Automatic versus manual evaluation. Evaluate relative to some predefined measure vs. evaluate by humans.
25 Parser evaluation Standard evaluation in parsing Intrinsic and automatic Parsers based on treebank grammars are evaluated by comparing their output to some gold standard. For this purpose, the treebank is customarily split into three sections: training, tuning, and testing. The parser is developed on training and tuning; final performance is reported on testing.
26 Parser evaluation Bracket score The standard measure to evaluate phrase structure parsers is bracket score. Bracket: [min, max, category] One compares the brackets found by the parser to the brackets in the gold standard tree. Performance is reported in terms of precision, recall, and F-score.
27 Parser evaluation Bracket score The standard measure to evaluate phrase structure parsers is bracket score. Bracket: [min, max, category] One compares the brackets found by the parser to the brackets in the gold standard tree. Performance is reported in terms of precision, recall, and F-score. signature!
28 Parser evaluation Evaluation measure Precision: Out of all brackets found by the parser, how many are also present in the gold standard? Recall: Out of all brackets in the gold standard, how many are also found by the parser? F1-score: harmonic mean between precision and recall: 2 precision recall / (precision + recall)
29 Parser evaluation F1-scores for the WSJ stupid your parser full grammar state of the art
30 More about treebanks
31 Parser evaluation Treebank types - examples Phrase-structure treebanks Penn treebank (English, and Chinese, Arabic) NEGRA (German) Dependency treebanks Prague Dep. treebank (Czech, + other) Danish Dep. treebank (Danish) Converted phrase-structured treebanks (e.g. Penn) Other CCGBank (CCG, English) LinGO Redwoods (HPSG, English)
32 Parser evaluation Swedish Treebank Combination of two older treebanks which have been merged and harmonized: SUC (Stockholm-Umeå Corpus) Talbanken Size: ~ tokens Phrase structure annotation with functional labels Converted to dependency annotation Some parts checked by humans, some annotated automatically
33 Parser evaluation Domains and languages Most of the parsing research is performed for English on the Wall Street Journal part of Penn Treebank Results for other English domains and for other languages are often worse than English WSJ Possible reasons Parsing methods developed for English tends to work best for English (WSJ) Language differences Annotation differences Treebank size and quality...
34 Parser evaluation Treebank annotation issues Not only one possible annotation Important to have clear guidelines Quality control in the annotation project
35 Parser evaluation Dependency annotation options John and Mary to eat the apple John Doe (a) Coordination (b) Infinitive Verbs (c) Noun Phrases (d) Noun Sequence of Rome can come (e) Prepositional Phrases (f) Verb Groups Schwartz et al. CoLING 2012.
36 Parser evaluation Univeral dependency treebank Stanford dependencies (de Marneffe et al, 2006),! adapted and harmonised for cross-lingual consistency Version 1.1:! English! Finnish! French! German! Italian! Indonesian! Japanese! Korean! Portuguese! Spanish! Swedish! March 2014 advmod p det nsubj Toutefois, les filles adorent les desserts. ADV PUNC DET NOUN VERB DET NOUN PUNC root dobj det p Version 1.0:! English! French! German! Korean! Spanish! Swedish! July 2013 Google part-of-speech tags (Petrov et al, 2012),! fine-grained language specific tags if available Slide from Joakim Nivre
37 Parser evaluation Universal dependency principles Maximize parallelism Don t annotate the same thing in different ways Don t make different things look the same Don t overdo it Don t annotate things that aren t there Languages select from a universal pool of categories Allow language-specific extensions Use content words as heads
38 Parser evaluation Univeral dependency parsing results Language Labeled attachment score German English Swedish Spanish French Korean From McDonald et al. ACL 2013.
39 Summary One can extract probabilistic context-free grammars from treebanks. Parsers can be evaluated by comparing their output against a gold standard. Reading: J&M 12.4, 14.3, 14.7
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