Machine Learning Group A Kernel-based Approach to Learning Semantic Parsers Rohit J. Kate Doctoral Dissertation Proposal Supervisor: Raymond J. Mooney Machine Learning Group Department of Computer Sciences University of Texas at Austin November 21, 2005 University of Texas at Austin Outline • Semantic Parsing • Related Work • Background on Kernel-based Methods • Completed Research • Proposed Research • Conclusions 2 Semantic Parsing • Semantic Parsing: Transforming natural language (NL) sentences into computer executable complete meaning representations (MRs) • Importance of Semantic Parsing – Natural language communication with computers – Insights into human language acquisition • Example application domains – CLang: Robocup Coach Language – Geoquery: A Database Query Application 3 CLang: RoboCup Coach Language • In RoboCup Coach competition teams compete to coach simulated players • The coaching instructions are given in a formal language called CLang Coach If our player 4 has the ball, our player 4 should shoot. Simulated soccer field Semantic Parsing CLang ((bowner our {4}) (do our {4} shoot)) 4 Geoquery: A Database Query Application • Query application for U.S. geography database containing about 800 facts [Zelle & Mooney, 1996] User Which rivers run through the states bordering Texas? Semantic Parsing Query answer(traverse_2( next_to(stateid(‘texas’)))) 5 Learning Semantic Parsers • Assume meaning representation languages (MRLs) have deterministic context free grammars – true for almost all computer languages – MRs can be parsed unambiguously 6 NL: Which rivers run through the states bordering Texas? MR: answer(traverse_2(next_to(stateid(‘texas’)))) Parse tree of MR: ANSWER RIVER answer TRAVERSE_2 traverse_2 STATE NEXT_TO STATE next_to STATEID stateid ‘texas’ Non-terminals: ANSWER, RIVER, TRAVERSE_2, STATE, NEXT_TO, STATEID Terminals: answer, traverse_2, next_to, stateid, ‘texas’ Productions: ANSWER answer(RIVER), RIVER TRAVERSE_2(STATE), STATE NEXT_TO(STATE), STATE NEXT_TO(STATE) TRAVERSE_2 traverse_2, NEXT_TO next_to, STATEID ‘texas’ 7 Learning Semantic Parsers • Assume meaning representation languages (MRLs) have deterministic context free grammars – true for almost all computer languages – MRs can be parsed unambiguously • Training data consists of NL sentences paired with their MRs • Induce a semantic parser which can map novel NL sentences to their correct MRs • Learning problem differs from that of syntactic parsing where training data has trees annotated over the NL sentences 8 Outline • Semantic Parsing • Related Work • Background on Kernel-based Methods • Completed Research • Proposed Research • Conclusions 9 Related Work: CHILL [Zelle & Mooney, 1996] • Uses Inductive Logic Programming (ILP) to induce a semantic parser • Learns rules to control actions of a deterministic shift-reduce parser • Processes sentence one word at a time making hard parsing decision each time • Brittle and ILP techniques do not scale to large corpora 10 Related Work: SILT [Kate, Wong & Mooney, 2005] • Transformation rules associate NL patterns with MRL templates NL pattern our left [3] penalty area MRL template AREA (left (penalty-area our)) • NL patterns matched in the sentence are replaced by the MRL templates • By the end of parsing, NL sentence gets transformed into its MR • Two versions: string patterns and syntactic tree patterns 11 Related Work: SILT contd. Weaknesses of SILT: • Hard-matching transformation rules are brittle: – For e.g. for NL pattern our left [3] penalty area “our left penalty area” “our left side of penalty area ” “left of our penalty area” “our ah.. left penalty area” • Parsing is done deterministically which is less robust than probabilistic parsing 12 Related Work: WASP [Wong, 2005] • Based on Synchronous Context-free Grammars • Uses Machine Translation technique of statistical word alignment to find good transformation rules • Builds a maximum entropy model for parsing • The transformation rules are hard-matching 13 Related Work: SCISSOR [Ge & Mooney, 2005] • 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 our player 2 has DT-null NN-null the ball 14 Related Work: Zettlemoyer & Collins [2005] • Uses Combinatorial Categorial Grammar (CCG) formalism to learn a statistical semantic parser • Generates CCG lexicon relating NL words to semantic types through general hand-built template rules • Uses maximum entropy model for compacting this lexicon and doing probabilistic CCG parsing 15 Outline • Semantic Parsing • Related Work • Background on Kernel-based Methods • Completed Research • Proposed Research • Conclusions 16 Traditional Machine Learning with Structured Data Examples Information loss Feature Engineering Feature Vectors Machine Learning Algorithm 17 Kernel-based Machine Learning with Structured Data Examples Implicit mapping to potentially infinite number of features Kernel Computations Kernelized Machine Learning Algorithm 18 Kernel Functions • A kernel K is a similarity function over domain X which maps any two objects x, y in X to their similarity score K(x,y) • For x1, x2 ,…, xn in X, the n-by-n matrix (K(xi,xj))ij should be symmetric and positive-semidefinite, then the kernel function calculates the dot-product of the implicit feature vectors in some highdimensional feature space • Machine learning algorithms which use the data only to compute similarity can be kernelized (e.g. Support Vector Machines, Nearest Neighbor etc.) 19 String Subsequence Kernel • Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] • All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” K(s,t) = ? 20 String Subsequence Kernel • Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] • All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = left K(s,t) = 1+? 21 String Subsequence Kernel • Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] • All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = our K(s,t) = 2+? 22 String Subsequence Kernel • Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] • All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = penalty K(s,t) = 3+? 23 String Subsequence Kernel • Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] • All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = area K(s,t) = 4+? 24 String Subsequence Kernel • Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] • All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = left penalty K(s,t) = 5+? 25 String Subsequence Kernel • Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] • All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” K(s,t) = 11 26 Normalized String Subsequence Kernel • Normalize the kernel (range [0,1]) to remove any bias due to different string lengths K normalized ( s , t ) K (s, t ) K ( s , s ) * K (t , t ) • Lodhi et al. [2002] give O(n|s||t|) for computing string subsequence kernel • Used for Text Categorization [Lodhi et al, 2002] and Information Extraction [Bunescu & Mooney, 2005b] 27 Support Vector Machines • Mapping data to high-dimensional feature spaces can lead to overfitting of training data (“curse of dimensionality”) • Support Vector Machines (SVMs) are known to be resistant to this overfitting 28 SVMs: Maximum Margin • Given positive and negative examples, SVMs find a separating hyperplane such that the margin ρ between the closest examples is maximized • Maximizing the margin is good according to intuition and PAC theory ρ Separating hyperplane 29 SVMs: Probability Estimates • Probability estimate of a point belonging to a class can be obtained using its distance from the hyperplane [Platt, 1999] 30 Why Kernel-based Approach to Learning Semantic Parsers? • Natural language sentences are structured • Natural languages are flexible, various ways to express the same semantic concept CLang MR: (left (penalty-area our)) NL: our left penalty area our left side of penalty area left side of our penalty area left of our penalty area our penalty area towards the left side our ah.. left penalty area 31 Why Kernel-based Approach to Learning Semantic Parsers? right side of our penalty area left of our penalty area opponent’s right penalty area our left side of penalty area our ah.. left penalty area our right midfield our left penalty area left side of our penalty area our penalty area towards the left side Kernel methods can robustly capture the range of NL contexts. 32 Outline • Semantic Parsing • Related Work • Background on Kernel-based Methods • Completed Research • Proposed Research • Conclusions 33 KRISP: Kernel-based Robust Interpretation by Semantic Parsing • Learns semantic parser from NL sentences paired with their respective MRs given MRL grammar • Productions of MRL are treated like semantic concepts • SVM classifier is trained for each production with string subsequence kernel • These classifiers are used to compositionally build MRs of the sentences 34 Overview of KRISP MRL Grammar NL sentences with MRs Collect positive and negative examples Train string-kernel-based SVM classifiers Training Testing Novel NL sentences Best semantic derivations (correct and incorrect) Semantic Parser Best MRs 35 Overview of KRISP MRL Grammar NL sentences with MRs Collect positive and negative examples Train string-kernel-based SVM classifiers Training Testing Novel NL sentences Best semantic derivations (correct and incorrect) Semantic Parser Best MRs 36 Overview of KRISP’s Semantic Parsing • We first define Semantic Derivation of an NL sentence • We define probability of a semantic derivation • Semantic parsing of an NL sentence involves finding its most probable semantic derivation • Straightforward to obtain MR from a semantic derivation 37 Semantic Derivation of an NL Sentence MR parse with non-terminals on the nodes: ANSWER RIVER answer TRAVERSE_2 traverse_2 STATE NEXT_TO STATE next_to STATEID stateid ‘texas’ Which rivers run through the states bordering Texas? 38 Semantic Derivation of an NL Sentence MR parse with productions on the nodes: ANSWER answer(RIVER) RIVER TRAVERSE_2(STATE) TRAVERSE_2 traverse_2 STATE NEXT_TO(STATE) NEXT_TO next_to STATE STATEID STATEID ‘texas’ Which rivers run through the states bordering Texas? 39 Semantic Derivation of an NL Sentence Semantic Derivation: Each node covers an NL substring: ANSWER answer(RIVER) RIVER TRAVERSE_2(STATE) TRAVERSE_2 traverse_2 STATE NEXT_TO(STATE) NEXT_TO next_to STATE STATEID STATEID ‘texas’ Which rivers run through the states bordering Texas? 40 Semantic Derivation of an NL Sentence Semantic Derivation: Each node contains a production and the substring of NL sentence it covers: (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO(STATE), [5..9]) (NEXT_TO next_to, [5..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? 1 2 3 4 5 6 7 8 9 41 Semantic Derivation of an NL Sentence Substrings in NL sentence may be in a different order: ANSWER answer(RIVER) RIVER TRAVERSE_2(STATE) TRAVERSE_2 traverse_2 STATE NEXT_TO(STATE) NEXT_TO next_to STATE STATEID STATEID ‘texas’ Through the states that border Texas which rivers run? 42 Semantic Derivation of an NL Sentence Nodes are allowed to permute the children productions from the original MR parse (ANSWER answer(RIVER), [1..10]) (RIVER TRAVERSE_2(STATE), [1..10]] (STATE NEXT_TO(STATE), [1..6]) (NEXT_TO next_to, [1..5]) (TRAVERSE_2 traverse_2, [7..10]) (STATE STATEID, [6..6]) (STATEID ‘texas’, [6..6]) Through the states that border Texas which rivers run? 1 2 3 4 5 6 7 8 9 10 43 Probability of a Semantic Derivation • Let Pπ(s[i..j]) be the probability that production π covers the substring s[i..j], • For e.g., PNEXT_TO next_to (“the states bordering”) (NEXT_TO next_to, [5..7]) the states bordering 5 6 7 • Obtained from the string-kernel-based SVM classifiers trained for each production π • Probability of a semantic derivation D: P(D ) P ( s [ i .. j ]) ( ,[ i .. j ]) D 44 Computing the Most Probable Semantic Derivation • Task of semantic parsing is to find the most probable semantic derivation • Let En,s[i..j], partial derivation, denote any subtree of a derivation tree with n as the LHS non-terminal of the root production covering sentence s from index i to j • Example of ESTATE,s[5..9] : (STATE NEXT_TO(STATE), [5..9]) (NEXT_TO next_to, [5..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) the states bordering Texas? 5 6 7 8 9 • Derivation D is then EANSWER, s[1..|s|] 45 Computing the Most Probable Semantic Derivation contd. • Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] • This is computed recursively as follows: E*STATE,s[5..9] (STATE NEXT_TO(STATE), [5..9]) the states bordering Texas? 5 E * n , s [ i .. j ] 6 7 8 9 makeTree ( arg max ( P ( s [ i .. j ]) )) n n1 .. n t G 46 Computing the Most Probable Semantic Derivation contd. • Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] • This is computed recursively as follows: E*STATE,s[5..9] (STATE NEXT_TO(STATE), [5..9]) E*NEXT_TO,s[i..j] E*STATE,s[i..j] the states bordering Texas? 5 E * n , s [ i .. j ] 6 7 8 9 makeTree ( arg max ( P ( s [ i .. j ]) )) n n1 .. n t G 47 Computing the Most Probable Semantic Derivation contd. • Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] • This is computed recursively as follows: E*STATE,s[5..9] (STATE NEXT_TO(STATE), [5..9]) E*NEXT_TO,s[5..5] E*STATE,s[6..9] the states bordering Texas? 5 E * n , s [ i .. j ] 6 7 8 9 makeTree ( arg max ( P ( s [ i .. j ]) )) n n1 .. n t G 48 Computing the Most Probable Semantic Derivation contd. • Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] • This is computed recursively as follows: E*STATE,s[5..9] (STATE NEXT_TO(STATE), [5..9]) E*NEXT_TO,s[5..6] E*STATE,s[7..9] the states bordering Texas? 5 E * n , s [ i .. j ] 6 7 8 9 makeTree ( arg max ( P ( s [ i .. j ]) )) n n1 .. n t G 49 Computing the Most Probable Semantic Derivation contd. • Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] • This is computed recursively as follows: E*STATE,s[5..9] (STATE NEXT_TO(STATE), [5..9]) E*NEXT_TO,s[5..7] E*STATE,s[8..9] the states bordering Texas? 5 E * n , s [ i .. j ] 6 7 8 9 makeTree ( arg max ( P ( s [ i .. j ]) )) n n1 .. n t G 50 Computing the Most Probable Semantic Derivation contd. • Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] • This is computed recursively as follows: E*STATE,s[5..9] (STATE NEXT_TO(STATE), [5..9]) E*NEXT_TO,s[5..8] E*STATE,s[9..9] the states bordering Texas? 5 E * n , s [ i .. j ] 6 7 8 9 makeTree ( arg max ( P ( s [ i .. j ]) )) n n1 .. n t G 51 Computing the Most Probable Semantic Derivation contd. • Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] • This is computed recursively as follows: E*STATE,s[5..9] (STATE NEXT_TO(STATE), [5..9]) E*NEXT_TO,s[i..j] E*STATE,s[i..j] the states bordering Texas? 5 E * n , s [ i .. j ] 6 7 makeTree ( arg max n n1 .. n t G ( p 1 ,..., p t ) partition ( s [ i .. j ], t ) 8 9 ( P ( s [ i .. j ]) P ( E * nk , pk ) )) k 1 .. t 52 Computing the Most Probable Semantic Derivation contd. • Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] • This is computed recursively as follows: E*STATE,s[5..9] (STATE NEXT_TO(STATE), [5..9]) E*STATE,s[i..j] E*NEXT_TO,s[i..j] the states bordering Texas? 5 E * n , s [ i .. j ] 6 7 makeTree ( arg max n n1 .. n t G ( p 1 ,..., p t ) partition ( s [ i .. j ], t ) 8 9 ( P ( s [ i .. j ]) P ( E * nk , pk ) )) k 1 .. t 53 Computing the Most Probable Semantic Derivation contd. • Implemented by extending Earley’s [1970] context-free grammar parsing algorithm • Predicts subtrees top-down and completes them bottom-up • Dynamic programming algorithm which generates and compactly stores each subtree once • Extended because: – Probability of a production depends on which substring of the sentence it covers – Leaves are not terminals but substrings of words 54 Computing the Most Probable Semantic Derivation contd. • Does a greedy approximation search, with beam width ω, and returns ω most probable derivations it finds • Uses a threshold θ to prune low probability trees 55 Overview of KRISP MRL Grammar NL sentences with MRs Collect positive and negative examples Train string-kernel-based SVM classifiers Best semantic derivations (correct and incorrect) Pπ(s[i..j]) Training Testing Novel NL sentences Semantic Parser Best MRs 56 KRISP’s Training Algorithm • Takes NL sentences paired with their respective MRs as input • Obtains MR parses • Proceeds in iterations • In the first iteration, for every production π: – Call those sentences positives whose MR parses use that production – Call the remaining sentences negatives 57 KRISP’s Training Algorithm contd. First Iteration STATE NEXT_TO(STATE) Positives Negatives •which rivers run through the states bordering texas? •what state has the highest population ? •what is the most populated state bordering oklahoma ? •which states have cities named austin ? •what states does the delaware river run through ? •what is the largest city in states that border california ? •what is the lowest point of the state with the largest area ? … … String-kernel-based SVM classifier PSTATENEXT_TO(STATE) (s[i..j]) 58 KRISP’s Training Algorithm contd. • Using these classifiers Pπ(s[i..j]), obtain the ω best semantic derivations of each training sentence • Some of these derivations will give the correct MR, called correct derivations, some will give incorrect MRs, called incorrect derivations • For the next iteration, collect positives from most probable correct derivation • Collect negatives from incorrect derivations with higher probability than the most probable correct derivation 59 KRISP’s Training Algorithm contd. Most probable correct derivation: (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO(STATE), [5..9]) (NEXT_TO next_to, [5..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? 1 2 3 4 5 6 7 8 9 60 KRISP’s Training Algorithm contd. Most probable correct derivation: Collect positive examples (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO(STATE), [5..9]) (NEXT_TO next_to, [5..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? 1 2 3 4 5 6 7 8 9 61 KRISP’s Training Algorithm contd. Incorrect derivation with probability greater than the most probable correct derivation: (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? 1 2 3 4 5 6 Incorrect MR: answer(traverse_2(stateid(‘texas’))) 7 8 9 62 KRISP’s Training Algorithm contd. Most Probable Correct derivation: Incorrect derivation: (ANSWER answer(RIVER), [1..9]) (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO (STATE), [5..9]) (NEXT_TO next_to, [5..7]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? (RIVER TRAVERSE_2(STATE), [1..9]) (STATE STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Which rivers run through the states bordering Texas? Traverse both trees in breadth-first order till the first nodes where their productions differ are found. 63 KRISP’s Training Algorithm contd. Most Probable Correct derivation: Incorrect derivation: (ANSWER answer(RIVER), [1..9]) (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO (STATE), [5..9]) (NEXT_TO next_to, [5..7]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? (RIVER TRAVERSE_2(STATE), [1..9]) (STATE STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Which rivers run through the states bordering Texas? Traverse both trees in breadth-first order till the first nodes where their productions differ are found. 64 KRISP’s Training Algorithm contd. Most Probable Correct derivation: Incorrect derivation: (ANSWER answer(RIVER), [1..9]) (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO (STATE), [5..9]) (NEXT_TO next_to, [5..7]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? (RIVER TRAVERSE_2(STATE), [1..9]) (STATE STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Which rivers run through the states bordering Texas? Traverse both trees in breadth-first order till the first nodes where their productions differ are found. 65 KRISP’s Training Algorithm contd. Most Probable Correct derivation: Incorrect derivation: (ANSWER answer(RIVER), [1..9]) (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO (STATE), [5..9]) (NEXT_TO next_to, [5..7]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? (RIVER TRAVERSE_2(STATE), [1..9]) (STATE STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Which rivers run through the states bordering Texas? Traverse both trees in breadth-first order till the first nodes where their productions differ are found. 66 KRISP’s Training Algorithm contd. Most Probable Correct derivation: Incorrect derivation: (ANSWER answer(RIVER), [1..9]) (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO (STATE), [5..9]) (NEXT_TO next_to, [5..7]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? (RIVER TRAVERSE_2(STATE), [1..9]) (STATE STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Which rivers run through the states bordering Texas? Traverse both trees in breadth-first order till the first nodes where their productions differ are found. 67 KRISP’s Training Algorithm contd. Most Probable Correct derivation: Incorrect derivation: (ANSWER answer(RIVER), [1..9]) (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO (STATE), [5..9]) (NEXT_TO next_to, [5..7]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? (RIVER TRAVERSE_2(STATE), [1..9]) (STATE STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Which rivers run through the states bordering Texas? Mark the words under these nodes. 68 KRISP’s Training Algorithm contd. Most Probable Correct derivation: Incorrect derivation: (ANSWER answer(RIVER), [1..9]) (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO (STATE), [5..9]) (NEXT_TO next_to, [5..7]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? (RIVER TRAVERSE_2(STATE), [1..9]) (STATE STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Which rivers run through the states bordering Texas? Mark the words under these nodes. 69 KRISP’s Training Algorithm contd. Most Probable Correct derivation: Incorrect derivation: (ANSWER answer(RIVER), [1..9]) (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO (STATE), [5..9]) (NEXT_TO next_to, [5..7]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? (RIVER TRAVERSE_2(STATE), [1..9]) (STATE STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Which rivers run through the states bordering Texas? Consider all the productions covering the marked words. Collect negatives for productions which cover any marked word in incorrect derivation but not in the correct derivation. 70 KRISP’s Training Algorithm contd. Most Probable Correct derivation: Incorrect derivation: (ANSWER answer(RIVER), [1..9]) (ANSWER answer(RIVER), [1..9]) (RIVER TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2 traverse_2, [1..4]) (STATE NEXT_TO (STATE), [5..9]) (NEXT_TO next_to, [5..7]) (TRAVERSE_2 traverse_2, [1..7]) (STATE STATEID, [8..9]) (STATEID ‘texas’, [8..9]) Which rivers run through the states bordering Texas? (RIVER TRAVERSE_2(STATE), [1..9]) (STATE STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Which rivers run through the states bordering Texas? Consider the productions covering the marked words. Collect negatives for productions which cover any marked word in incorrect derivation but not in the correct derivation. 71 KRISP’s Training Algorithm contd. Next Iteration STATE NEXT_TO(STATE) Positives Negatives •the states bordering texas? •what state has the highest population ? •state bordering oklahoma ? •what states does the delaware river run through ? •states that border california ? •which states have cities named austin ? •states which share border •what is the lowest point of the state with the largest area ? •next to state of iowa •which rivers run through states bordering … … String-kernel-based SVM classifier PSTATENEXT_TO(STATE) (s[i..j]) 72 KRISP’s Training Algorithm contd. • In the next iteration, SVM classifiers are trained with the new positive examples and the accumulated negative examples • Iterate specified number of times 73 Experimental Corpora • CLang – 300 randomly selected pieces of coaching advice from the log files of the 2003 RoboCup Coach Competition – 22.52 words on average in NL sentences – 13.42 tokens on average in MRs • Geo250 [Zelle & Mooney, 1996] – 250 queries for the given U.S. geography database – 6.76 words on average in NL sentences – 6.20 tokens on average in MRs • Geo880 [Tang & Mooney, 2001] – Superset of Geo250 with 880 queries – 7.48 words on average in NL sentences – 6.47 tokens on average in MRs 74 Experimental Methodology • Evaluated using standard 10-fold cross validation • Correctness – CLang: output exactly matches the correct representation – Geoquery: the resulting query retrieves the same answer as the correct representation • Metrics Precision Number Number Recall of of test sentences correct MRs with complete Number of correct Number of test sentences output MRs MRs 75 Experimental Methodology contd. • Compared Systems: – – – – SILT [Kate, Wong & Mooney, 2005] WASP [Wong, 2005] SCISSOR [Ge & Mooney, 2005] CHILL • COCKTAIL ILP algorithm [Tang & Mooney, 2001] – Zettlemoyer & Collins (2005) • Different Experimental Setup (600 training, 280 testing examples) • Results available only for Geo880 corpus – Geobase • Hand-built NL interface [Borland International, 1988] • Results available only for Geo250 76 Experimental Methodology contd. • KRISP gives probabilities for its semantic derivation which are taken as confidences of the MRs • We plot precision-recall curves by first sorting the best MR for each sentence by confidences and then finding precision for every recall value • WASP and SCISSOR also output confidences so we show their precision-recall curves • Results of other systems shown as points on precision-recall graphs 77 Results on CLang requires more annotation on corpus CHILL gives 49.2% precision and 12.67% recall with 160 examples, can’t run beyond. 78 Results on Geo250 79 Results on Geo880 80 Results on Multilingual Geo250 • We have Geo250 corpus translated into Japanese, Spanish and Turkish • KRISP is directly applicable to other languages 81 Results on Multilingual Geo250 82 Outline • Semantic Parsing • Related Work • Background on Kernel-based Methods • Completed Research • Proposed Research – Short-term – Long term • Conclusions 83 Short Term: Exploiting Natural Language Syntax • KRISP currently uses only word order of the sentence • Semantic interpretation depends largely on NL syntax, exploiting it should help semantic parsing • We already have syntactic annotations on our corpora, used in SILT-tree and SCISSOR • Existing syntactic parsers can be trained on our corpora in addition to WSJ [Bikel, 2004] 84 Exploiting Natural Language Syntax contd. • Most natural extension of KRISP is to use syntactic-tree kernel instead of string kernel • Syntactic-tree kernel – Introduced by Collins & Duffy [2001] – K(x,y) = Number of subtrees common between x & y NP NP NP JJ left NP PP NN side IN of NP PRP$ JJ NN NN our penalty area left PP NN side IN of NP DT NN the midfield K(x,y) = ? 85 Exploiting Natural Language Syntax contd. • Most natural extension of KRISP is to use syntactic-tree kernel instead of string kernel • Syntactic-tree kernel – Introduced by Collins & Duffy [2001] – K(x,y) = Number of subtrees common between x & y NP NP NP JJ left NP PP NN side IN of NP PRP$ JJ NN NN our penalty area left PP NN side IN of NP DT NN the midfield K(x,y) = 1+? 86 Exploiting Natural Language Syntax contd. • Most natural extension of KRISP is to use syntactic-tree kernel instead of string kernel • Syntactic-tree kernel – Introduced by Collins & Duffy [2001] – K(x,y) = Number of subtrees common between x & y NP NP NP JJ left NP PP NN side IN of NP PRP$ JJ NN NN our penalty area left PP NN side IN of NP DT NN the midfield K(x,y) = 2+? 87 Exploiting Natural Language Syntax contd. • Most natural extension of KRISP is to use syntactic-tree kernel instead of string kernel • Syntactic-tree kernel – Introduced by Collins & Duffy [2001] – K(x,y) = Number of subtrees common between x & y NP NP NP JJ left NP PP NN side IN of NP PRP$ JJ NN NN our penalty area left PP NN side IN of NP DT NN the midfield K(x,y) = 3+? 88 Exploiting Natural Language Syntax contd. • Most natural extension of KRISP is to use syntactic-tree kernel instead of string kernel • Syntactic-tree kernel – Introduced by Collins & Duffy [2001] – K(x,y) = Number of subtrees common between x & y NP NP NP JJ left NP PP NN side IN of NP PRP$ JJ NN NN our penalty area left PP NN side IN of NP DT NN the midfield K(x,y) = 8 89 Exploiting Natural Language Syntax contd. • Often the syntactic information needed is present in dependency trees, full syntactic trees not necessary • Dependency trees capture most important functional relationship between words • Various dependency tree kernels have been used successfully for doing Information Extraction [Zelenko, Aone & Richardella, 2003], [Cumby & Roth, 2003], [Culotta & Sorenson, 2004], [Bunescu & Mooney, 2005a] 90 Short Term: Noisy NL Sentences • If users are interacting with the semantic parser through speech then many ways noise can be present [Zue & Glass, 2000] – Speech recognition errors – Interjections (um’s and ah’s) – Environment noise (door slams, phone rings etc.) – Out-of-domain words and ill-formed utterances • In KRISP, presence of extra words or corrupted words may decrease kernel values but won’t affect semantic parsing in a hard way • KRISP is hence more robust to noise compared to systems with hard-matching rules like SILT and WASP, or systems doing complete syntactic-semantic parsing like SCISSOR 91 Noisy NL Sentences contd. • We plan to do preliminary experiments by artificially corrupting our existing corpora • Then we plan to get and experiment on some real world noisy corpus 92 Short Term: Committees of Semantic Parsers System Correct CLang MRs out of 300 KRISP 178 WASP 185 SCISSOR 232 Committee Upper-bound on Correct MRs KRISP+WASP 223 KRISP+SCISSOR 253 WASP+SCISSOR 246 KRISP+WASP+SCISSOR 259 • Good indication that forming their committee will improve performance. 93 Committees of Semantic Parsers contd. Two general approaches to combine parse trees [Henderson & Brill, 1999] • Parser Switching: Learn which parser works best on which types of sentences • Parse Hybridization: Look into output MRs and combine their best components – Particularly useful when none of the parser generates complete MRs Prior work is specific to combining syntactic parses. We plan to explore these two general approaches for combining MRs. 94 Long Term: Non-parallel Training Corpus • Training data contained NL sentences aligned with their respective MRs • In some domains many NL sentences and semantic MRs may be available but not aligned – For e.g. in RoboCup commentary task [Binsted et al., 2000], NL sentences and symbolic description of events are available but not aligned • Referential ambiguity: Which NL description refers to which symbolic description? • In our present work we resolve which portion of the sentence refers to which production of MR parse • Same approach could be extended to one level higher 95 Non-parallel Training Corpus contd. • Let training corpus be {(Mi, Si)|i=1..N} where each Mi is a set of MRs and each Si is a set of NL sentences • Align every MR in Mi to every NL sentence in Si for i=1..N • Use KRISP’s training algorithm to learn classifiers • Find best alignment for MRs and NL sentences in (Mi, Si) by semantic parsing using these classifiers • Repeat till alignments don’t change • We plan to first do preliminary experiments by artificially making our corpus non-parallel and then extracting the alignments then test on a real-world corpus 96 Long Term: Complex Relation Extraction • Bunescu & Mooney [2005b] use string-based kernel to extract binary relation “protein-protein interaction” from text • This can be viewed as learning for an MRL grammar with only one production INTERACTION PROTEIN PROTEIN • Complex relation is an n-ary relation among n typed entities [McDonald et al., 2005] – For example, (person, job, company) NL sentence: John Smith is the CEO of Inc. Corp. Extraction: (John Smith, CEO, Inc. Corp.) 97 Complex Relation Extraction contd. (person, job,company) (person, job) (job, company) John Smith is the CEO of Inc. Corp. KRISP should be applicable to extract complex relations by treating it like higher level production composed of lower level productions. 98 Conclusions • KRISP: A new kernel-based approach to learning semantic parser • String-kernel-based SVM classifiers trained for each MRL production • Classifiers used to compositionally build complete MRs of NL sentences • Evaluated on two real-world corpora – Performs better than deterministic rule-based systems – Performs comparable to recent statistical systems • Proposed work: exploit NL syntax, form committees and broaden application domains 99 Thank You! Questions?? 10 0 Extra: Dealing with Constants • MRL grammar may contain productions corresponding to constants in the domain: STATEID ‘new york’ RIVERID ‘colorado’ NUM ‘2’ STRING ‘DR4C10’ • User can specify these as constant productions giving their NL substrings • Classifiers are not learned for these productions • Matching substring’s probability is taken as 1 • If n constant productions have same substring then each gets probability of 1/n STATEID ‘colorado’ RIVERID ‘colorado’ 10 1 Extra: Better String Subsequence Kernel • Subsequences with gaps should be downweighted • Decay factor λ in the range of (0,1] penalizes gaps • All subsequences are the implicit features and penalties are the feature values s = “left side of our penalty area” t = “our left penalty area” u = left penalty K(s,t) = 4+? 10 2 Extra: Better String Subsequence Kernel • Subsequences with gaps should be downweighted • Decay factor λ in the range of (0,1] penalizes gaps • All subsequences are the implicit features and penalties are the feature values Gap of 3 => λ3 s = “left side of our penalty area” Gap of 0 => λ0 t = “our left penalty area” u = left penalty K(s,t) = 4+λ3*λ0 +? 10 3 Extra: Better String Subsequence Kernel • Subsequences with gaps should be downweighted • Decay factor λ in the range of (0,1] penalizes gaps • All subsequences are the implicit features and penalties are the feature values s = “left side of our penalty area” t = “our left penalty area” K(s,t) = 4+3λ+3 λ3+ λ5 10 4 Extra: KRISP’s Training Algorithm contd. • What if none of the ω most probable derivations of a sentence is correct? • Extended Earley’s algorithm can be forced to derive only the correct derivations by making sure all subtrees it generates exist in the correct MR parse 10 5 Extra: N-best MRs for Geo880 10 6 Extra: KRISP’s Average Running Times Corpus Average Training Time (minutes) Average Testing Time (minutes) Geo250 1.44 0.05 Geo880 18.1 0.65 CLang 58.85 3.18 Average running times per fold in minutes taken by KRISP. 10 7 Extra: KRISP’s Learning PR Curves on CLang 10 8 Extra: KRISP’s Learning PR Curves on Geo250 10 9 Extra: KRISP’s Learning PR Curves on Geo880 11 0 Extra: Experimental Methodology • Correctness – CLang: output exactly matches the correct representation – Geoquery: the resulting query retrieves the same answer as the correct representation If the ball is in our penalty area, all our players except player 4 should stay in our half. Correct: ((bpos (penalty-area our)) (do (player-except our{4}) (pos (half our))) ((bpos (penalty-area opp)) Output: (do (player-except our{4}) (pos (half our))) 11 1 Extra: 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 DIRECTIVE do TEAM UNUM ACTION ACTION shoot 11 2

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# Content-Boosted Collaborative Filtering