Using and teaching logic and machine learning for modeling cognitive processes
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1 Using and teaching logic and machine learning for modeling cognitive processes Anders Søgaard Center for Language Technology University of Copenhagen Njalsgade DK-2300 Copenhagen S soegaard@hum.ku.dk November
2 Running example: dependency parsing MA Programme in IT & Cognition Student experiences Recent initiatives Logic for dependency parsing Machine learning for dependency parsing Logic and machine learning?
3 Dependency parsing subj obj John smokes Lebanese XP XPsubj smokes XPobj John Lebanese 1 John NP 2 SUBJ 2 smokes V 0 ROOT 3 Lebanese NP 2 OBJ The 1-best parsing problem for projective dependency grammars is in O( G n 3 ). Non-projective dependency parsing is NP-hard in general (e.g. by the Traveling Salesman Problem). Popular approximate parsing algorithms exist for both projective (deterministic transition-based; linear time) and non-projective dependency parsing (minimum spanning tree, O( G n 2 ).
4 Graph-based dependency parsing (MST) Edmonds (1969) introduced a two-step O( G n 2 ) minimum spanning tree algorithm for edge-factored models: (i) greedy head selection (ii) cycle contraction.
5 Graph-based dependency parsing (MST) Edmonds (1969) introduced a two-step O( G n 2 ) minimum spanning tree algorithm for edge-factored models: (i) greedy head selection (ii) cycle contraction. John smokes Lebanese =
6 Graph-based dependency parsing (MST) Edmonds (1969) introduced a two-step O( G n 2 ) minimum spanning tree algorithm for edge-factored models: (i) greedy head selection (ii) cycle contraction. John smokes Lebanese = John (smokes Lebanese) =
7 Graph-based dependency parsing (MST) Edmonds (1969) introduced a two-step O( G n 2 ) minimum spanning tree algorithm for edge-factored models: (i) greedy head selection (ii) cycle contraction. John smokes Lebanese = John (smokes Lebanese) = John smokes Lebanese
8 Transition-based dependency parsing (i) Shift... w w...
9 Transition-based dependency parsing (i) (ii) Shift... w w... Reduce... w iff v.v w
10 Transition-based dependency parsing (i) (ii) (iii) Shift... w w... Reduce... w iff v.v w Left-Arc... w v v... add w v
11 Transition-based dependency parsing (i) (ii) (iii) (iv) Shift... w w... Reduce... w iff v.v w Left-Arc... w v v... add w v Right-Arc... v w v, w... add v w iff w.w w
12 How did John get to smoke Libanese? Shift... John smokes Lebanese Left-Arc..., John smokes Lebanese Shift... smokes Lebanese John smokes Right-arc..., smokes Lebanese Reduce... smokes, Lebanese... smokes Lebanese Root... smokes...
13 MA Programme in IT & Cognition Computer Science Language Technology Linguistics Psychology
14 MA Programme in IT & Cognition Computer Science Language Technology Linguistics Psychology
15 MA Programme in IT & Cognition Computer Science Language Technology Linguistics Psychology st 2nd 3rd 4th RCS (F) Form.Ling. RCS(A) Thesis Logic CP(F) CP(A) Exp. mthd. Stats Adapt.Syst. Linguistics LT(F) LT(A) Progr(F/A) HCI(F) HCI(A) RCS(F) and RCS(A) are compulsary.
16 Student experiences Coherence Flexibility Level Social Thesis support Good Bad
17 Student experiences Coherence Flexibility Level Social Thesis support Good Bad Students never stayed at the university after class to work in groups.
18 Student experiences Coherence Flexibility Level Social Thesis support Good Bad Students never stayed at the university after class to work in groups. Students did not know much about each other.
19 Student experiences Coherence Flexibility Level Social Thesis support Good Bad Students never stayed at the university after class to work in groups. Students did not know much about each other. Several students did not have a thesis topic ready after having completed the first 90 ECTS.
20 Recent initiatives
21 Recent initiatives Mentoring, i.e. monthly interviews about: coherence (1st year) courses and exams extra-curricular activities thesis (primarily 2nd year)
22 Recent initiatives Mentoring, i.e. monthly interviews about: coherence (1st year) courses and exams extra-curricular activities thesis (primarily 2nd year) Student groups: a. ensemble-based part-of-speech tagging b. text prediction c. text classification d. model-checking for extensions of modal logic e. word alignment in translated text Collaboration: a. Center for Language Technology b. Mikroværkstedet, Lund University d. University of Tübingen (Germany) e. Copenhagen Business School
23 Evening lectures: J. Hansen (RUC): Dynamic epistemic logic P. Lindström (Lund, Sweden): How children learn math M. Haulrich (CBS): Repair in transition-based parsing R. Dekova (BAS, Bulgaria): Lexical semantics and the mental lexicon
24 Evening lectures: J. Hansen (RUC): Dynamic epistemic logic P. Lindström (Lund, Sweden): How children learn math M. Haulrich (CBS): Repair in transition-based parsing R. Dekova (BAS, Bulgaria): Lexical semantics and the mental lexicon Reading groups, workshops, etc.: CBS-RG Machine Learning CBS-RG Natural Language Processing Linguistic Circle of Copenhagen ACL 10 (Uppsala, Sweden) ESSLLI 10
25 Challenge: 90 ECTS and a diverse group of students 1. Students do stay at the university after class. 2. The student groups average out the students. 3. Student groups are also a chance for excellent students to excel. 4. Finally, however, we synchronized our courses a bit:
26 Challenge: 90 ECTS and a diverse group of students 1. Students do stay at the university after class. 2. The student groups average out the students. 3. Student groups are also a chance for excellent students to excel. 4. Finally, however, we synchronized our courses a bit: (a) RCS(F) and RCS(A) model topics introduced in CP(F).
27 Challenge: 90 ECTS and a diverse group of students 1. Students do stay at the university after class. 2. The student groups average out the students. 3. Student groups are also a chance for excellent students to excel. 4. Finally, however, we synchronized our courses a bit: (a) RCS(F) and RCS(A) model topics introduced in CP(F). (b) Topics from (a) are reused in other courses (Logic, LT(F), etc.).
28 Challenge: 90 ECTS and a diverse group of students 1. Students do stay at the university after class. 2. The student groups average out the students. 3. Student groups are also a chance for excellent students to excel. 4. Finally, however, we synchronized our courses a bit: (a) RCS(F) and RCS(A) model topics introduced in CP(F). (b) Topics from (a) are reused in other courses (Logic, LT(F), etc.). (c) All course exercises are in Python/Orange, also used in the student groups.
29 Logic for dependency parsing In the dependency graph: 1 : p, q 2 : p, q 3 : p, q 4 : p, q the formula q, i.e. the current node precedes a node whose syntactic head is in the denotation of q, evaluates as true in nodes 1 and 2. the formula ; is not true in any node.
30 Logic for dependency parsing (cont d) A modal logic for dependency parsing was first introduced in Bröker (1997).
31 Logic for dependency parsing (cont d) A modal logic for dependency parsing was first introduced in Bröker (1997). Kepser (2008) uses monadic second order logic to query dependency treebanks.
32 Logic for dependency parsing (cont d) A modal logic for dependency parsing was first introduced in Bröker (1997). Kepser (2008) uses monadic second order logic to query dependency treebanks. Søgaard (2009) uses hybrid logic and crude repair to improve accuracy of dependency parsers.
33 Logic for dependency parsing (cont d) A modal logic for dependency parsing was first introduced in Bröker (1997). Kepser (2008) uses monadic second order logic to query dependency treebanks. Søgaard (2009) uses hybrid logic and crude repair to improve accuracy of dependency parsers. Modal logics for other parsing formalisms are presented in Keller (1993), Søgaard (2007) and Søgaard and Lange (2009).
34 Machine learning for dependency parsing Dependency parsing is typically cast as supervised learning.
35 Machine learning for dependency parsing Dependency parsing is typically cast as supervised learning. Sufficient labeled data exists for a wide variety of languages. The CONLL-X Shared Task used datasets from 12 languages. The CONLL 2007 Shared Task used datasets from 10 languages (with three repeats). Labeled data exists for other languages, incl. Hebrew, Latin, Romanian, Thai.
36 Machine learning for dependency parsing Dependency parsing is typically cast as supervised learning. Sufficient labeled data exists for a wide variety of languages. The CONLL-X Shared Task used datasets from 12 languages. The CONLL 2007 Shared Task used datasets from 10 languages (with three repeats). Labeled data exists for other languages, incl. Hebrew, Latin, Romanian, Thai. The CONLL format and evaluation procedure are standard in the community.
37 Exercise from Lect. 1, RCS(F): Naive Bayes Features: pos(w ). Class: pos(w). Labeled data: 1 John NP 2 2 drives V 0 3 cars NP 2 1 John NP 3 2 is V 3 3 fast ADJ 0 1 John NP 2 2 walks V 0 3 and CONJ 2 4 talks V 2 5 fast ADJ 2 P( Root) 3/11 P( V) 6/11 P( ADJ) 2/11 d = NP d = V d = ADJ P(d Root) 0/3 P(d Root) 2/3 P(d Root) 1/3 P(d V) 3/6 P(d V) 1/6 P(d V) 1/6 P(d ADJ) 1/2 P(d ADJ) 1/2 P(d ADJ) 0/2 0.09: V 0.19 : Root 0.09:Root/V If single-rooted: 1 John 2 2 drives 0 3 fast 2
38 Dependency parsing, now Algorithm Learner Complexity MaltParser Transition-based SVM O( G n) MSTParser Graph-based MIRA O( G n 2 )* *Faster on average, since models are smaller.
39 Dependency parsing, now Algorithm Learner Complexity MaltParser Transition-based SVM O( G n) MSTParser Graph-based MIRA O( G n 2 )* *Faster on average, since models are smaller. Nivre and McDonald (2008) use stacking to combine the strengths of the two parsers.
40 Dependency parsing, now Algorithm Learner Complexity MaltParser Transition-based SVM O( G n) MSTParser Graph-based MIRA O( G n 2 )* *Faster on average, since models are smaller. Nivre and McDonald (2008) use stacking to combine the strengths of the two parsers. Martins et al. (2008) use recursive stacking to obtain previously best reported results.
41 Dependency parsing, now Algorithm Learner Complexity MaltParser Transition-based SVM O( G n) MSTParser Graph-based MIRA O( G n 2 )* *Faster on average, since models are smaller. Nivre and McDonald (2008) use stacking to combine the strengths of the two parsers. Martins et al. (2008) use recursive stacking to obtain previously best reported results. Semisupervised methods have also been used to boost state-of-the-art (Koo et al., 2008; Sagae and Gordon, 2009; Suzuki et al., 2009).
42 Dependency parsing, now Algorithm Learner Complexity MaltParser Transition-based SVM O( G n) MSTParser Graph-based MIRA O( G n 2 )* *Faster on average, since models are smaller. Nivre and McDonald (2008) use stacking to combine the strengths of the two parsers. Martins et al. (2008) use recursive stacking to obtain previously best reported results. Semisupervised methods have also been used to boost state-of-the-art (Koo et al., 2008; Sagae and Gordon, 2009; Suzuki et al., 2009). Søgaard (t.a.) combines ensemble-based and semisupervised methods to obtain best reported results.
43 Søgaard (t.a.) C 1 (F) C 2 (F) C 3 (F) Ens C 1 (R 1 ) C 2 (R 1 ) C 3 (R 1 ) Sem C 1 (R 1 + U 2,3 ) C 2 (R 1 + U 1,3 ) C 3 (R 1 + U 1,2 ) Ens C 1 (R 2 ) C 2 (R 2 ) C 3 (R 2 ).
44 CONLL-X datasets C06 Mar08 Ours Arabic Danish Dutch German Japanese Portuguese Slovene Spanish Swedish Turkish
45 Checked items: Logic and machine learning? (2do-list) Model-checking is used to verify labeled data. Non-checked items:
46 Logic and machine learning? (2do-list) Checked items: Model-checking is used to verify labeled data. Crude repair is used to improve parsing quality. Non-checked items:
47 Checked items: Logic and machine learning? (2do-list) Model-checking is used to verify labeled data. Crude repair is used to improve parsing quality. Finally, logic is used to study the properties of linguistic theories (Blackburn and Spaan, 1993; Søgaard, 2007). Non-checked items:
48 Checked items: Logic and machine learning? (2do-list) Model-checking is used to verify labeled data. Crude repair is used to improve parsing quality. Finally, logic is used to study the properties of linguistic theories (Blackburn and Spaan, 1993; Søgaard, 2007). Non-checked items: Transition-based dependency parsing in Cross-style probabilistic modal logic.
49 Checked items: Logic and machine learning? (2do-list) Model-checking is used to verify labeled data. Crude repair is used to improve parsing quality. Finally, logic is used to study the properties of linguistic theories (Blackburn and Spaan, 1993; Søgaard, 2007). Non-checked items: Transition-based dependency parsing in Cross-style probabilistic modal logic. Learning crude repair functions.
50 Checked items: Logic and machine learning? (2do-list) Model-checking is used to verify labeled data. Crude repair is used to improve parsing quality. Finally, logic is used to study the properties of linguistic theories (Blackburn and Spaan, 1993; Søgaard, 2007). Non-checked items: Transition-based dependency parsing in Cross-style probabilistic modal logic. Learning crude repair functions. Modal characterizations of mildly non-projective dependency grammars.
51 Checked items: Logic and machine learning? (2do-list) Model-checking is used to verify labeled data. Crude repair is used to improve parsing quality. Finally, logic is used to study the properties of linguistic theories (Blackburn and Spaan, 1993; Søgaard, 2007). Non-checked items: Transition-based dependency parsing in Cross-style probabilistic modal logic. Learning crude repair functions. Modal characterizations of mildly non-projective dependency grammars. Model-checking polyadic dynamic logic.
52 Data-driven dependency parsing in collaborative research projects at CST Question answering: MOSES (university websites); led by Patrizia Paggio. ESICT (patient diagnosis); led by Bente Maegaard. Machine translation: ESSMT (practical); led by me. EMCOTT (theoretical; under review); led by Jürgen Wedekind and me.
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