Machine Learning Group Learning for Semantic Parsing of

Machine Learning Group Learning for Semantic Parsing of Natural Language Raymond J. Mooney Ruifang Ge, Rohit Kate, Yuk Wah Wong John Zelle, Cynthia Thompson Machine Learning Group Department of Computer Sciences University of Texas at Austin July 31, 2005 University of Texas at Austin

Syntactic Natural Language Learning • Most computational research in natural-language learning has addressed “low-level” syntactic processing. – – Morphology (e. g. past-tense generation) Part-of-speech tagging Shallow syntactic parsing Syntactic parsing 2

Semantic Natural Language Learning • Learning for semantic analysis has been restricted to relatively small, isolated tasks. – Word sense disambiguation (e. g. SENSEVAL) – Semantic role assignment (determining agent, patient, instrument, etc. , e. g. Frame. Net, Prop. Bank) – Information extraction 3

Semantic Parsing • A semantic parser maps a natural-language sentence to a complete, detailed semantic representation (logical form). • For many applications, the desired output is immediately executable by another program. • Two application domains: – CLang: Robo. Cup Coach Language – Geo. Query: A Database Query Application 4

CLang: Robo. Cup Coach Language • In Robo. Cup Coach competition teams compete to coach simulated players • The coaching instructions are given in a formal language called CLang Coach If the ball is in our penalty area, then all our players except player 4 should stay in our half. Simulated soccer field Semantic Parsing CLang ((bpos (penalty-area our)) (do (player-except our{4}) (pos (half our))) 5

Geo. Query: A Database Query Application • Query application for U. S. geography database containing about 800 facts [Zelle & Mooney, 1996] How many cities are there in the US? User Semantic Parsing Query answer(A, count(B, (city(B), loc(B, C), const(C, countryid(USA))), A)) 6

Learning Semantic Parsers • Manually programming robust semantic parsers is difficult due to the complexity of the task. • Semantic parsers can be learned automatically from sentences paired with their logical form. NL LF Training Exs Natural Language Semantic-Parser Learner Semantic Parser Logical Form 7

Engineering Motivation • Most computational language-learning research strives for broad coverage while sacrificing depth. – “Scaling up by dumbing down” • Realistic semantic parsing currently entails domain dependence. • Domain-dependent natural-language interfaces have a large potential market. • Learning makes developing specific applications more tractable. • Training corpora can be easily developed by tagging existing corpora of formal statements with natural-language glosses. 8

Cognitive Science Motivation • Most natural-language learning methods require supervised training data that is not available to a child. – General lack of negative feedback on grammar. – No POS-tagged or treebank data. • Assuming a child can infer the likely meaning of an utterance from context, NL LF pairs are more cognitively plausible training data. 9

Our Semantic-Parser Learners • CHILL+WOLFIE (Zelle & Mooney, 1996; Thompson & Mooney, 1999, 2003) – Separates parser-learning and semantic-lexicon learning. – Learns a deterministic parser using ILP techniques. • SILT (Kate, Wong & Mooney, 2005) – Learns symbolic transformation rules for mapping directly from NL to LF. • SCISSOR (Ge & Mooney, 2005) – Integrates semantic interpretation into a statistical syntactic parser (Collins model-3). 10

CHILL (Zelle & Mooney, 1992 -96) • Semantic parser acquisition system using Inductive Logic Programming (ILP) to induce a parser written in Prolog. • Starts with a parsing “shell” written in Prolog and learns to control the operators of this parser to produce the given I/O pairs. • Requires a semantic lexicon, which for each word gives one or more possible semantic representations. • Parser must disambiguate words, introduce proper semantic representations for each, and then put them together in the right way to produce a proper representation of the sentence. 11

CHILL Example • U. S. Geographical database – Sample training pair • Cuál es el capital del estado con la población más grande? • answer(C, (capital(S, C), largest(P, (state(S), population(S, P))))) – Sample semantic lexicon • • • cuál : capital: estado: más grande: población: answer(_, _) capital(_, _) state(_) largest(_, _) population(_, _) 12

WOLFIE (Thompson & Mooney, 1995 -1999) • Learns a semantic lexicon for CHILL from the same corpus of semantically annotated sentences. • Determines hypotheses for word meanings by finding largest isomorphic common subgraphs shared by meanings of sentences in which the word appears. • Uses a greedy-covering style algorithm to learn a small lexicon sufficient to allow compositional construction of the correct representation from the words in a sentence. 13

WOLFIE + CHILL Semantic Parser Acquisition NL LF Training Exs WOLFIE Lexicon Learner Semantic Lexicon CHILL Parser Learner Natural Language Semantic Parser Logical Form 14

Semantic Parsing using Transformation Rules • SILT (Semantic Interpretation by Learning Transformations) • Uses pattern-based transformation rules which map natural language phrases to formal language constructs • Transformation rules are repeatedly applied to the sentence to construct its formal language expression 15

Formal Language Grammar NL: If our player 4 has the ball, our player 4 should shoot. CLang: ((bowner our {4}) (do our {4} shoot)) CLang Parse: RULE CONDITION bowner DIRECTIVE TEAM UNUM our 4 do TEAM UNUM ACTION our 4 shoot • Non-terminals: RULE, CONDITION, ACTION… • Terminals: bowner, our, 4… • Productions: RULE CONDITION DIRECTIVE do TEAM UNUM ACTION shoot 16

Transformation Rule Representation • Rule has two components: a natural language pattern and an associated formal language template • Two versions of SILT: – String-based rules: used to convert natural language word gap sentence directly to formal language String-pattern TEAM [1] ball tree to formal – Tree-based rules: used to UNUM converthassyntactic language Template CONDITION (bowner TEAM {UNUM}) S Treepattern VP NP TEAM Template UNUM NP VBZ has DT NN the ball CONDITION (bowner TEAM {UNUM}) 17

Example of Semantic Parsing If our player 4 has the ball, our player 4 should shoot. our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 18

Example of Semantic Parsing If TEAM our player 4 has the ball, TEAM our player 4 should shoot. our our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 19

Example of Semantic Parsing If TEAM player 4 has the ball, TEAM player 4 should shoot. our our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 20

Example of Semantic Parsing UNUM 4 has the ball, TEAM player UNUM 4 should shoot. If TEAM player our 4 our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 21

Example of Semantic Parsing If TEAM UNUM has the ball, TEAM our 4 our UNUM should shoot. 4 our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 22

Example of Semantic Parsing If TEAM UNUM has the ball, TEAM our 4 our UNUM should ACTION shoot. 4 our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 23

Example of Semantic Parsing If TEAM UNUM has the ball, TEAM our 4 our UNUM should ACTION. 4 our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 24

Example of Semantic Parsing CONDITION If TEAM UNUM has the ball , TEAM our (bowner 4 our {4}) our UNUM should ACTION. 4 our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 25

Example of Semantic Parsing If , TEAM CONDITION (bowner our {4}) our UNUM should ACTION. 4 our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 26

Example of Semantic Parsing If , TEAM CONDITION (bowner our {4}) our UNUM DIRECTIVE should ACTION. (do our 4 {4} shoot) our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 27

Example of Semantic Parsing If , CONDITION . DIRECTIVE (bowner our {4}) (do our {4} shoot) our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 28

Example of Semantic Parsing If CONDITION RULE , . DIRECTIVE (bowner ((bowner ourour {4}) (do our {4} shoot)) (do our {4} shoot) our player 4 shoot TEAM our UNUM 4 ACTION shoot TEAM UNUM has [1] ball If CONDITION, DIRECTIVE. CONDITION (bowner TEAM {UNUM}) RULE (CONDITION DIRECTIVE) TEAM UNUM should ACTION DIRECTIVE (do TEAM {UNUM} ACTION) 29

Learning Transformation Rules • SILT induces rules from a corpora of NL sentences paired with their formal representations • Patterns are learned for each production by bottom -up rule learning • For every production: – Call those sentences positives whose formal representations’ parses use that production – Call the remaining sentences negatives 30

Rule Learning for a Production CONDITION (bpos REGION) positives • • The ball is in REGION , our player 7 is in REGION and no opponent is around our player 7 within 1. 5 distance. If the ball is in REGION and not in REGION then player 3 should intercept the ball. During normal play if the ball is in the REGION then player 7 , 9 and 11 should dribble the ball to the REGION. When the play mode is normal and the ball is in the REGION then our player 2 should pass the ball to the REGION. All players except the goalie should pass the ball to REGION if it is in RP 18. If the ball is inside rectangle ( -54 , -36 , 0 , 36 ) then player 10 should position itself at REGION with a ball attraction of REGION. Player 2 should pass the ball to REGION if it is in REGION. negatives • • If our player 6 has the ball then he should take a shot on goal. If player 4 has the ball , it should pass the ball to player 2 or 10. If the condition DR 5 C 3 is true , then player 2 , 3 , 7 and 8 should pass the ball to player 3. During play on , if players 6 , 7 or 8 is in REGION , they should pass the ball to players 9 , 10 or 11. If "Clear_Condition" , players 2 , 3 , 7 or 5 should clear the ball REGION. If it is before the kick off , after our goal or after the opponent's goal , position player 3 at REGION. If the condition MDR 4 C 9 is met , then players 4 -6 should pass the ball to player 9. If Pass_11 then player 11 should pass to player 9 and no one else. • SILT applies greedy-covering, bottom-up rule induction method that repeatedly generalizes positives until they start covering negatives 31

Generalization of String Patterns ACTION (pos REGION) Pattern 1: Always position player UNUM at REGION. Pattern 2: Whenever the ball is in REGION, position player UNUM near the REGION. Find the highest scoring common subsequence: 32

Generalization of String Patterns ACTION (pos REGION) Pattern 1: Always position player UNUM at REGION. Pattern 2: Whenever the ball is in REGION, position player UNUM near the REGION. Find the highest scoring common subsequence: Generalization: position player UNUM [2] REGION. 33

Generalization of Tree Patterns REGION (penalty-area TEAM) Pattern 1: Pattern 2 NP NP NN NN TEAM POS penalty box NP PRP$ NN NN TEAM penalty area ’s Find common subgraphs. 34

Generalization of Tree Patterns REGION (penalty-area TEAM) Pattern 1: Pattern 2 NP NP PRP$ NN NN TEAM penalty area TEAM POS penalty box ’s NP Find common subgraphs. * Generalization: NP NN NN TEAM penalty 35

Rule Learning for a Production CONDITION (bpos REGION) positives negatives • • The ball is in REGION , our player 7 is in REGION and no opponent is around our player 7 within 1. 5 distance. If the ball is in REGION and not in REGION then player 3 should intercept the ball. During normal play if the ball is in the REGION then player 7 , 9 and 11 should dribble the ball to the REGION. When the play mode is normal and the ball is in the REGION then our player 2 should pass the ball to the REGION. All players except the goalie should pass the ball to REGION if it is in REGION. If the ball is inside REGION then player 10 should position itself at REGION with a ball attraction of REGION. Player 2 should pass the ball to REGION if it is in REGION. • • If our player 6 has the ball then he should take a shot on goal. If player 4 has the ball , it should pass the ball to player 2 or 10. If the condition DR 5 C 3 is true , then player 2 , 3 , 7 and 8 should pass the ball to player 3. During play on , if players 6 , 7 or 8 is in REGION , they should pass the ball to players 9 , 10 or 11. If "Clear_Condition" , players 2 , 3 , 7 or 5 should clear the ball REGION. If it is before the kick off , after our goal or after the opponent's goal , position player 3 at REGION. If the condition MDR 4 C 9 is met , then players 4 -6 should pass the ball to player 9. If Pass_11 then player 11 should pass to player 9 and no one else. Bottom-up Rule Learner ball is [2] REGION it is in REGION CONDITION (bpos REGION) 36

Rule Learning for a Production CONDITION (bpos REGION) positives negatives • • The CONDITION , our player 7 is in REGION and no opponent is around our player 7 within 1. 5 distance. If the CONDITION and not in REGION then player 3 should intercept the ball. During normal play if the CONDITION then player 7 , 9 and 11 should dribble the ball to the REGION. When the play mode is normal and the CONDITION then our player 2 should pass the ball to the REGION. All players except the goalie should pass the ball to REGION if CONDITION. If the CONDITION then player 10 should position itself at REGION with a ball attraction of REGION. Player 2 should pass the ball to REGION if CONDITION. • • If our player 6 has the ball then he should take a shot on goal. If player 4 has the ball , it should pass the ball to player 2 or 10. If the condition DR 5 C 3 is true , then player 2 , 3 , 7 and 8 should pass the ball to player 3. During play on , if players 6 , 7 or 8 is in REGION , they should pass the ball to players 9 , 10 or 11. If "Clear_Condition" , players 2 , 3 , 7 or 5 should clear the ball REGION. If it is before the kick off , after our goal or after the opponent's goal , position player 3 at REGION. If the condition MDR 4 C 9 is met , then players 4 -6 should pass the ball to player 9. If Pass_11 then player 11 should pass to player 9 and no one else. Bottom-up Rule Learner ball is [2] REGION it is in REGION CONDITION (bpos REGION) 37

Rule Learning for All Productions • Transformation rules for productions should cooperate globally to generate complete semantic parses • Redundantly cover every positive example by β = 5 best rules coverage accuracy • Find the subset of these rules which best cooperate to generate complete semantic parses on the training data 38

SCISSOR: Semantic Composition that Integrates Syntax and Semantics to get Optimal Representations • Based on a fairly standard approach to compositional semantics [Jurafsky and Martin, 2000] • A statistical parser is used to generate a semantically augmented parse tree (SAPT) – Augment Collins’ head-driven model 2 (Bikel’s S-bowner implementation, 2004) to incorporate semantic labels NP-player VP-bowner • Translate a complete formal meaning PRP$-team SAPT NN-playerinto CD-unum VB-bowner NP-null representation (MR) 2 our player has DT-null NN-null MR: bowner(player(our, 2)) the ball 39

Overview of SCISSOR NL Sentence SAPT Training Examples learner Integrated Semantic Parser SAPT TRAINING TESTING Compose. MR MR 40

Compose. MR bowner player team player our player bowner unum 2 null bowner has null the ball 41

Compose. MR bowner(_) player(_, _) team player(_, _) our player bowner(_) unum 2 null bowner(_) has null the ball 42

Compose. MR bowner(player(our, 2)) bowner(_) player(our, 2) player(_, _) team player(_, _) our player unum 2 null bowner(_) has null the ball player(team, unum) bowner(player) 43

Collins’ Head-Driven Model 2 • A generative, lexicalized model • Each node on the tree has a syntactic label, it is also lexicalized with its head word S(has) NP(player) VP(has) NP(ball) PRP$ NN CD VB DT NN our player 2 has the ball 44

Modeling Rule Productions as Markov Processes S(has) VP(has) Ph(VP | S, has) 45

Modeling Rule Productions as Markov Processes S(has) VP(has) {NP } { } Ph(VP | S, has) × Plc({NP} | S, VP, has) × Prc({} | S, VP, has) 46

Modeling Rule Productions as Markov Processes S(has) NP(player) VP(has) {NP } { } Ph(VP | S, has) × Plc({NP} | S, VP, has) × Prc({} | S, VP, has) × Pd(NP(player) | S, VP, has, LEFT, {NP}) 47

Modeling Rule Productions as Markov Processes S(has) NP(player) VP(has) {} {} Ph(VP | S, has) × Plc({NP} | S, VP, has) × Prc({} | S, VP, has) × Pd(NP(player) | S, VP, has, LEFT, {NP}) 48

Modeling Rule Productions as Markov Processes S(has) STOP NP(player) VP(has) {} {} Ph(VP | S, has) × Plc({NP} | S, VP, has) × Prc({} | S, VP, has) × Pd(NP(player) | S, VP, has, LEFT, {NP}) × Pd(STOP | S, VP, has, LEFT, {}) 49

Modeling Rule Productions as Markov Processes S(has) STOP NP(player) VP(has) {} {} STOP Ph(VP | S, has) × Plc({NP} | S, VP, has) × Prc({} | S, VP, has) × Pd(NP(player) | S, VP, has, LEFT, {NP}) × Pd(STOP | S, VP, has, LEFT, {}) × Pd(STOP | S, VP, has, RIGHT, {}) 50

Integrating Semantics into the Model • Use the same Markov processes • Add a semantic label to each node • Add semantic subcat frames – Give semantic subcategorization preferences – bowner takes a player as its argument S(has) S-bowner(has ) NP-player(player) NP(player) VP(has) VP-bowner(has) NP(ball) NP-null(ball) PRP$-team CD-unum VB-bowner NN-null PRP$ NN-player NN CD VB DTDT-null. NN our player 2 2 has the ball 51

Adding Semantic Labels into the Model S-bowner(has) VP-bowner(has) Ph(VP-bowner | S-bowner, has) 52

Adding Semantic Labels into the Model S-bowner(has) VP-bowner(has) {NP}-{player} { }-{ } Ph(VP-bowner | S-bowner, has) × Plc({NP}-{player} | S-bowner, VP-bowner, has) × Prc({}-{}| S-bowner, VP-bowner, has) 53

Adding Semantic Labels into the Model S-bowner(has) NP-player(player) VP-bowner(has) {NP}-{player} { }-{ } Ph(VP-bowner | S-bowner, has) × Plc({NP}-{player} | S-bowner, VP-bowner, has) × Prc({}-{}| S-bowner, VP-bowner, has) × Pd(NP-player(player) | S-bowner, VP-bowner, has, LEFT, {NP}-{player}) 54

Adding Semantic Labels into the Model S-bowner(has) NP-player(player) VP-bowner(has) { }-{ } Ph(VP-bowner | S-bowner, has) × Plc({NP}-{player} | S-bowner, VP-bowner, has) × Prc({}-{}| S-bowner, VP-bowner, has) × Pd(NP-player(player) | S-bowner, VP-bowner, has, LEFT, {NP}-{player}) 55

Adding Semantic Labels into the Model S-bowner(has) STOP NP-player(player) VP-bowner(has) { }-{ } Ph(VP-bowner | S-bowner, has) × Plc({NP}-{player} | S-bowner, VP-bowner, has) × Prc({}-{}| S-bowner, VP-bowner, has) × Pd(NP-player(player) | S-bowner, VP-bowner, has, LEFT, {NP}-{player}) × Pd(STOP | S-bowner, VP-bowner, has, LEFT, {}-{}) 56

Adding Semantic Labels into the Model S-bowner(has) STOP NP-player(player) VP-bowner(has) STOP { }-{ } Ph(VP-bowner | S-bowner, has) × Plc({NP}-{player} | S-bowner, VP-bowner, has) × Prc({}-{}| S-bowner, VP-bowner, has) × Pd(NP-player(player) | S-bowner, VP-bowner, has, LEFT, {NP}-{player}) × Pd(STOP | S-bowner, VP-bowner, has, LEFT, {}-{}) × Pd(STOP | S-bowner, VP-bowner, has, RIGHT, {}-{}) 57

Smoothing • Each label in SAPT is the combination of a syntactic label and a semantic label • Increases data sparsity • Use Bayes rule to break the parameters down Ph(H | P, w) = Ph(Hsyn, Hsem | P, w) = Ph(Hsyn | P, w) × Ph(Hsem | P, w, Hsyn) • Details in the paper 58

Experimental Corpora • CLang – 300 randomly selected pieces of coaching advice from the log files of the 2003 Robo. Cup Coach Competition – 22. 52 words on average in NL sentences – 14. 24 tokens on average in formal expressions • Geo. Query [Zelle & Mooney, 1996] – 250 queries for the given U. S. geography database – 6. 87 words on average in NL sentences – 5. 32 tokens on average in formal expressions 59

Experimental Methodology • Evaluated using standard 10 -fold cross validation • Syntactic parses for tree-based version of SILT were obtained by training Collins’ parser [Bikel, 2004] on WSJ treebank and gold-standard parses of training sentences • Correctness – CLang: output exactly matches the correct representation – Geoquery: the resulting query retrieves the same answer as the correct representation • Metrics 60

Compared Systems • CHILL [Tang & Mooney, 2001] – Learn control rules for parsing based on Inductive Logic Programming (ILP) • SILT [Kate, Wong, and Mooney, 2005] – Learns pattern-based transformation rules – SILT-string – SILT-tree • SCISSOR [Ge and Mooney, 2005] – Integrated syntactic/semantic statistical parser • GEOBASE – The original hand-built parser on Geoquery 61

Precision Learning Curve for CLang 62

Recall Learning Curve for CLang 63

F 1 Measure Learning Curve for CLang 64

Precision Learning Curve for Geoquery 65

Recall Learning Curve for Geoquery 66

F 1 Measure Learning Curve for Geoquery 67

Related Work • [Zettlemoyer and Collins, 2005] use Combinatorial Categorial Grammar (CCG) formalism to learn a statistical semantic parser. – Requires an initial CCG syntactic grammar of English. • PRECISE [Popescu, 2003] – Designed to work specially on NL database interfaces – Not a learning system • [Miller et al. , 1996; Miller et al. , 2000] use an approach similar to SCISSOR to train a statistical parser integrating syntax and semantics. – Does not utilize subcat information – Task: Information Extraction 68

Future Work • Explore methods that can automatically generate SAPTs to minimize the annotation effort for SCISSOR. • Use of methods from statistical MT (e. g. word alignment) for semantic parsing. • Learning semantic parsers just from sentences paired with “perceptual context. ” 69

Contextually Ambiguous Sentence Meaning • Sentences are uttered in complex situations composed of numerous potential meanings. • Could assume each sentence is annotated with multiple possible meanings inferred from context (Siskind, 1996). – Multiple instance learning (Dietterich et al. , 1997) • Assuming context meaning is represented as a semantic network, sentence meaning could be assumed to be any connected subgraph of the context. 70

Sample Ambiguous Context “Juvenile caresses canine. ” Dog isa Chewing Has. Color attr obj Black Spot patient Petting agent patient Thing 1 isa Mary isa Child Has. Color attr obj Blonde Possess patient agent isa Thing 2 Barbie obj part isa Has. Part Doll Hair Bone 71

Sample Ambiguous Context “Juvenile caresses canine. ” Dog isa Chewing Has. Color attr obj Black Spot patient Petting agent patient Thing 1 isa Mary isa Child Has. Color attr obj Blonde Possess patient agent isa Thing 2 Barbie obj part isa Has. Part Doll Hair Bone 72

Sample Ambiguous Context “Juvenile caresses canine. ” Dog isa Chewing Has. Color attr obj Black Spot patient Petting agent patient Thing 1 isa Mary isa Child Has. Color attr obj Blonde Possess patient agent isa Thing 2 Barbie obj part isa Has. Part Doll Hair Bone 73

Sample Ambiguous Context “Juvenile caresses canine. ” Dog isa Chewing Has. Color attr obj Black Spot patient Petting agent patient Thing 1 isa Mary isa Child Has. Color attr obj Blonde Possess patient agent isa Thing 2 Barbie obj part isa Has. Part Doll Hair Bone 74

Sample Ambiguous Context “Juvenile caresses canine. ” Dog isa Chewing Has. Color attr obj Black Spot patient Petting agent patient Thing 1 isa Mary isa Child Has. Color attr obj Blonde Possess patient agent isa Thing 2 Barbie obj part isa Has. Part Doll Hair Bone 75

Sample Ambiguous Context “Juvenile caresses canine. ” Dog isa Chewing Has. Color attr obj Black Spot patient Petting agent patient Thing 1 isa Mary isa Child Has. Color attr obj Blonde Possess patient agent isa Thing 2 Barbie obj part isa Has. Part Doll Hair Bone 76

Sample Ambiguous Context “Juvenile caresses canine. ” Dog isa Chewing Has. Color attr obj Black Spot patient Petting agent patient Thing 1 isa Mary isa Child Has. Color attr obj Blonde Possess patient agent isa Thing 2 Barbie obj part isa Has. Part Doll Hair Bone 77

Sample Ambiguous Context “Juvenile caresses canine. ” Dog isa Chewing Has. Color attr obj Black Spot patient Petting agent patient Thing 1 isa Mary isa Child Has. Color attr obj Blonde Possess patient agent isa Thing 2 Barbie obj part isa Has. Part Doll Hair Bone 78

Sample Ambiguous Context “Juvenile caresses canine. ” Dog isa Chewing Has. Color attr obj Black Spot patient Petting agent patient Thing 1 isa Mary isa Child Has. Color attr obj Blonde Possess patient agent isa Thing 2 Barbie obj part isa Has. Part Doll Hair Bone 79

Conclusions • Learning semantic parsers is an important and challenging problem in natural-language learning. • We have obtained promising results on several applications using a variety of approaches with different strengths and weaknesses. • Not many others have explored this problem, I would encourage others to consider it. • More and larger corpora are needed for training and testing semantic parser induction. 80

Thank You! Our papers on learning semantic parsers are on-line at: http: //www. cs. utexas. edu/~ml/publication/lsp. html Our corpora can be downloaded from: http: //www. cs. utexas. edu/~ml/nldata. html Questions? ? 81