INF4820: Algorithms for Artificial Intelligence and Natural Language Processing. Live Coding, Parser Evaluation, Quiz
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1 University of Oslo : Department of Informatics INF4820: Algorithms for Artificial Intelligence and Natural Language Processing Live Coding, Parser Evaluation, Quiz Stephan Oepen & Erik Velldal Language Technology Group (LTG) November 11, 2015
2 Overview Last Time Generalized Chart Parsing Inside the Parse Forest Viterbi Tree Decoding
3 Overview Last Time Generalized Chart Parsing Inside the Parse Forest Viterbi Tree Decoding Today Exhaustive Unpacking Viterbi Tree Decoding Parser Evaluation Wrap-Up Quiz
4 Chart Parsing: Key Ideas The parse chart is a two-dimensional matrix of edges (aka chart items); an edge is a (possibly partial) rule instantiation over a substring of input; the chart indexes edges by start and end string position (aka vertices); dot in rule RHS indicates degree of completion: α β 1...β i 1 β i...β n ; active edges (aka incomplete items) partial RHS: [1, 2, VP V NP]; passive edges (aka complete items) full RHS: [1, 3, VP V NP ]; The Fundamental Rule [i, j, α β 1...β i 1 β i...β n ] + [j, k, β i γ + ] [i, k, α β 1...β i β i+1...β n ] Chart Parsing Wrap-Up (2) inf nov-15 (oe@ifi.uio.no)
5 Ambiguity Packing in the Chart General Idea Maintain only one edge for each α from i to j (the representative ); record alternate sequences of daughters for α in the representative. Implementation Group passive edges into equivalence classes by identity of α, i, and j; search chart for existing equivalent edge (h, say) for each new edge e; when h (the host edge) exists, pack e into h to record equivalence; e not added to the chart, no derivations with or further processing of e; unpacking multiply out all alternative daughters for all result edges. Chart Parsing Wrap-Up (3) inf nov-15 (oe@ifi.uio.no)
6 An Example (Hypothetical) Parse Forest Chart Parsing Wrap-Up (4) inf nov-15
7 Unpacking: Cross-Multiplying Local Ambiguity How many complete trees in total? Chart Parsing Wrap-Up (5) inf nov-15 (oe@ifi.uio.no)
8 Live Coding: Exhaustive Unpacking Chart Parsing Wrap-Up (6) inf nov-15
9 Probability Theory and Natural Language? The most important questions of life are, for the most part, really only questions of probability. (Pierre-Simon Laplace, 1812) Chart Parsing Wrap-Up (7) inf nov-15
10 Probability Theory and Natural Language? The most important questions of life are, for the most part, really only questions of probability. (Pierre-Simon Laplace, 1812) Special wards in lunatic asylums could well be populated with mathematicians who have attempted to predict random events from finite data samples. (Richard A. Epstein, 1977) Chart Parsing Wrap-Up (7) inf nov-15
11 Probability Theory and Natural Language? The most important questions of life are, for the most part, really only questions of probability. (Pierre-Simon Laplace, 1812) Special wards in lunatic asylums could well be populated with mathematicians who have attempted to predict random events from finite data samples. (Richard A. Epstein, 1977) But it must be recognized that the notion probability of a sentence is an entirely useless one, under any known interpretation of this term. (Noam Chomsky, 1969) Chart Parsing Wrap-Up (7) inf nov-15 (oe@ifi.uio.no)
12 Probability Theory and Natural Language? The most important questions of life are, for the most part, really only questions of probability. (Pierre-Simon Laplace, 1812) Special wards in lunatic asylums could well be populated with mathematicians who have attempted to predict random events from finite data samples. (Richard A. Epstein, 1977) But it must be recognized that the notion probability of a sentence is an entirely useless one, under any known interpretation of this term. (Noam Chomsky, 1969) Every time I fire a linguist, system performance improves. (Fredrick Jelinek, 1980s) Chart Parsing Wrap-Up (7) inf nov-15 (oe@ifi.uio.no)
13 Viterbi Decoding over the Parse Forest Recall the Viterbi algorithm for HMMs v i (x)= L max k=1 [v i 1(k) P(x k) P(o i x)] Over the (complete, result edges from the) parse forest, compute Viterbi scores for sub-trees of increasing size: v(e)=max P(β 1,...β n α) v(β i ) Similar to HMM decoding, we also need to keep track of the set of daughters that led to the maximum probability. i
14 Parser Evaluation There are a number of aspects to consider in judging parser performance: Coverage the percentage of inputs for which we we found an analysis.
15 Parser Evaluation There are a number of aspects to consider in judging parser performance: Coverage the percentage of inputs for which we we found an analysis. Overgeneration the percentage of ungrammatical inputs (incorrectly) assigned an analysis.
16 Parser Evaluation There are a number of aspects to consider in judging parser performance: Coverage the percentage of inputs for which we we found an analysis. Overgeneration the percentage of ungrammatical inputs (incorrectly) assigned an analysis. Efficiency time and memory used by the parser.
17 Parser Evaluation There are a number of aspects to consider in judging parser performance: Coverage the percentage of inputs for which we we found an analysis. Overgeneration the percentage of ungrammatical inputs (incorrectly) assigned an analysis. Efficiency time and memory used by the parser. Accuracy Sentence accuracy measures the percentage of input sentences which received the correct tree.
18 Parser Evaluation There are a number of aspects to consider in judging parser performance: Coverage the percentage of inputs for which we we found an analysis. Overgeneration the percentage of ungrammatical inputs (incorrectly) assigned an analysis. Efficiency time and memory used by the parser. Accuracy Sentence accuracy measures the percentage of input sentences which received the correct tree. Since full trees can be quite complex, this is a very strict metric, and so most statistical parsers report accuracy according to the granular ParsEval metric.
19 ParsEval The ParsEval metric (Black, et al., 1991) measures constituent overlap. The original formulation only considered the shape of the (unlabeled) bracketing. The modern standard uses a tool calledevalb, which reports precision, recall and F 1 score for labeled brackets, as well as the number of crossing brackets.
20 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house))))
21 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6np
22 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6np 0,1 dt
23 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6np 0,1 dt 1,3 advp
24 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 0,1 dt 1,3 advp
25 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 0,1 dt 2,3 jj 1,3 advp
26 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 0,1 dt 2,3 jj 1,3 advp 3,6 nom
27 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 3,4 nn 0,1 dt 2,3 jj 1,3 advp 3,6 nom
28 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 3,4 nn 0,1 dt 2,3 jj 4,5 pos 1,3 advp 3,6 nom
29 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 3,4 nn 0,1 dt 2,3 jj 4,5 pos 1,3 advp 3,6 nom 5,6 nn
30 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) 0,6 np 1,2 rb 3,4 nn 0,1 dt 2,3 jj 4,5 pos 1,3 advp 3,6 nom 5,6 nn System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6np 2,6nom 3,4nn 0,1dt 2,3jj 4,5pos 1,2jj 3,6nom 5,6nn
31 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 3,4 nn 0,1 dt 2,3 jj 4,5 pos 1,3 advp 3,6 nom 5,6 nn Correct: 7 0,6np 2,6nom 3,4nn 0,1dt 2,3jj 4,5pos 1,2jj 3,6nom 5,6nn
32 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 3,4 nn 0,1 dt 2,3 jj 4,5 pos 1,3 advp 3,6 nom 5,6 nn Recall: Correct Gold = 7 9 Precision: Correct System = 7 9 0,6np 2,6nom 3,4nn 0,1dt 2,3jj 4,5pos 1,2jj 3,6nom 5,6nn
33 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 3,4 nn 0,1 dt 2,3 jj 4,5 pos 1,3 advp 3,6 nom 5,6 nn Recall: Correct Gold = 7 9 Precision: Correct System = 7 9 0,6np 2,6nom 3,4nn 0,1dt 2,3jj 4,5pos 1,2jj 3,6nom 5,6nn F 1 score: 7 9
34 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 3,4 nn 0,1 dt 2,3 jj 4,5 pos 1,3 advp 3,6 nom 5,6 nn Recall: Correct Gold = 2 3 Precision: Correct System = 2 3 0,6np 2,6nom 3,4nn 0,1dt 2,3jj 4,5pos 1,2jj 3,6nom 5,6nn F 1 score: 2 3
35 ParsEval Gold Standard (ADVP (RB pretty) (JJ big)) (NN house)) ) System Output (JJ pretty) (NOM (JJ big) (NN house)))) 0,6 np 1,2 rb 3,4 nn 0,1 dt 2,3 jj 4,5 pos 1,3 advp 3,6 nom 5,6 nn Recall: Correct Gold = 2 3 Crossing Brackets: 1 Precision: Correct System = 2 3 0,6np 2,6nom 3,4nn 0,1dt 2,3jj 4,5pos 1,2jj 3,6nom 5,6nn F 1 score: 2 3
36 In Conclusion In the second half of the class, we set out to determine: which string is most likely: How to recognise speech vs. How to wreck a nice beach
37 In Conclusion In the second half of the class, we set out to determine: which string is most likely: How to recognise speech vs. How to wreck a nice beach
38 In Conclusion In the second half of the class, we set out to determine: which string is most likely: How to recognise speech vs. How to wreck a nice beach which tag sequence is most likely for flies like flowers: NNS VB NNS vs. VBZ P NNS
39 In Conclusion In the second half of the class, we set out to determine: which string is most likely: How to recognise speech vs. How to wreck a nice beach which tag sequence is most likely for flies like flowers: NNS VB NNS vs. VBZ P NNS
40 In Conclusion In the second half of the class, we set out to determine: which string is most likely: How to recognise speech vs. How to wreck a nice beach which tag sequence is most likely for flies like flowers: NNS VB NNS vs. VBZ P NNS which syntactic analysis is most likely: S VP I VBD NP ate N PP with tuna NP sushi S VP I VBD NP PP with tuna NP ate N sushi
41 In Conclusion In the second half of the class, we set out to determine: which string is most likely: How to recognise speech vs. How to wreck a nice beach which tag sequence is most likely for flies like flowers: NNS VB NNS vs. VBZ P NNS which syntactic analysis is most likely: S VP I VBD NP ate N PP with tuna NP sushi S VP I VBD NP PP with tuna NP ate N sushi
42 Finally: Need Some Bonus Points? Rules of the Game Up to four bonus points towards completion of Obligatory Exercise (3).
43 Finally: Need Some Bonus Points? Rules of the Game Up to four bonus points towards completion of Obligatory Exercise (3). Get one post-it; at the top, write down your first and last name. Further, write down your UiO account name (e.g.oe, in my case).
44 Finally: Need Some Bonus Points? Rules of the Game Up to four bonus points towards completion of Obligatory Exercise (3). Get one post-it; at the top, write down your first and last name. Further, write down your UiO account name (e.g.oe, in my case). Write each answer on a line of its own, prefix by question number.
45 Finally: Need Some Bonus Points? Rules of the Game Up to four bonus points towards completion of Obligatory Exercise (3). Get one post-it; at the top, write down your first and last name. Further, write down your UiO account name (e.g.oe, in my case). Write each answer on a line of its own, prefix by question number. Do not consult with your neighbors; they might just mess things up.
46 Finally: Need Some Bonus Points? Rules of the Game Up to four bonus points towards completion of Obligatory Exercise (3). Get one post-it; at the top, write down your first and last name. Further, write down your UiO account name (e.g.oe, in my case). Write each answer on a line of its own, prefix by question number. Do not consult with your neighbors; they might just mess things up. After the Quiz Post your answers at the front of your table, we will collect all notes. Discuss your answers with your neighbor(s); find out who is right.
47 Question (1): Natural Language Ambiguity Assume the following toy grammar of English: S NP NP Det N N NN Det the N kitchen gold towel rack
48 Question (1): Natural Language Ambiguity Assume the following toy grammar of English: S NP NP Det N N NN Det the N kitchen gold towel rack (1) How many different syntactic analyses, if any, does the grammar assign to the following strings? (a) the kitchen towel rack (b) the kitchen gold towel rack
49 Question (2): CKY Parsing Assume the following grammar and CKY parse table: S NP VP VP VNP VP VP PP NP NP VP PP PNP NP S S 1 V VP VP 2 NP NP 3 P PP 4 NP
50 Question (2): CKY Parsing Assume the following grammar and CKY parse table: S NP VP VP VNP VP VP PP NP NP VP PP PNP NP S S 1 V VP VP 2 NP NP 3 P PP 4 NP (2) Which pair(s) of input cells and which production(s) gave rise to the derivation of category S in target cell 0, 5?
51 Question (3): Packed Parse Forests
52 Question (3): Packed Parse Forests (3) How many complete trees are represented in this forest?
53 Question (4): Parser Evaluation S NP Det N the N N kitchen N N towel rack S NP Det N the N N N N rack kitchen towel
54 Question (4): Parser Evaluation S NP Det N the N N kitchen N N towel rack S NP Det N the N N N N rack kitchen towel (4) What are the ParsEval precision and recall scores for this pair of trees (gold on the left; system on the right)?
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