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Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2007) Learning for Semantic Parsing Advisor: Hsin-His.

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Presentation on theme: "Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2007) Learning for Semantic Parsing Advisor: Hsin-His."— Presentation transcript:

1 Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2007) Learning for Semantic Parsing Advisor: Hsin-His Chen Reporter: Chi-Hsin Yu Date: Raymond J. Mooney Department of Computer Sciences, University of Texas at Austin

2 Outlines Introduction Sample Applications and their MRLs Systems for Learning Semantic Parsers Experimental Evaluation Future Research

3 Introduction Semantic parsing  the task of mapping a natural language sentence into a complete, formal meaning representation (MR) or logical form Meaning representation language (MRL)  a formal unambiguous language that allows for automated inference and processing  such as first-order predicate logic

4 Introduction (cont.) MRL of this paper  is “executable” and can be directly used by another program to perform some task, such as answering questions from a database controlling the actions of a real or simulated robot The goal of these systems  is to induce an efficient and accurate semantic parser that can map novel sentences into this MRL Training corpus  sentences annotated (NL, MR) pairs  extra training input: such as syntactic parse trees or semantically annotated parse trees

5 Sample Applications and their MRLs Database query language  a sample database on U.S. geography  logical query language based on Prolog Coaching language for robotic soccer  developed for the RoboCup Coach Competition  a formal language called CLANG Tactics and behaviors are expressed in terms of if-then rules

6 Systems for Learning Semantic Parsers Three approaches to learning statistical semantic parsers  SCISSOR (CoNLL-2005, COLING-ACL-06) adds detailed semantics to a statistical syntactic parser  WASP (HLT/NAACL-06) adapts statistical machine translation methods to map from NL to MRL  KRISP (COLING-ACL-06) uses SVM with a subsequence kernel specialized for text learning

7 Systems for Learning Semantic Parsers – SCISSOR SCISSOR  Semantic Composition that Integrates Syntax and Semantics to get Optimal Representations  learns a statistical parser that generates a semantically augmented parse tree (SAPT)  Training data: (NL, SAPT, MR) triples Process  (1) an enhanced version of Collin’s parser (head-driven model 2) is trained to produce SAPTs  (2) a recursive procedure is used to compositionally construct the MR for each node in the SAPT given the MRs of its children

8 Systems for Learning Semantic Parsers – SCISSOR (cont.) Ball owner (type concept) Predicate concept

9 Systems for Learning Semantic Parsers – WASP WASP  Word Alignment-based Semantic Parsing  uses Statistical Machine Translation (SMT) techniques (parallel corpora)  to translate from NL to MRL Process  (1) An SMT word alignment system, GIZA++ is used to produce an N to 1 alignment between the words in the NL sentence and a sequence of MRL productions.  (2) A synchronous CFG (SCFG) produces complete MRs by combining these NL substrings and their translations.

10 Systems for Learning Semantic Parsers – WASP (cont.)

11 Systems for Learning Semantic Parsers – WRISP KRISP  Kernel-based Robust Interpretation for Semantic Parsing  uses SVMs with string kernels to build semantic parsers Process  (1) learns classifiers: a word or phrase  a particular concept in the MRL  (2) learns classifiers: NL substrings  a production  (3) each classifier estimates the probability of each production covering different substrings of the sentence.

12 Systems for Learning Semantic Parsers – WRISP (cont.)

13 Experimental Evaluation (1) Two corpora of NL sentences paired with MRs  CLANG the average NL sentence length: words 300 pieces of coaching advice  GEOQUERY the average NL sentence length: 6.87 words 250 questions manually translated into logical form

14 Experimental Evaluation (2) Evaluation  10-fold cross validation  Recall: % sentences resulted in complete MRs  Precision: % MRs that were correct CLANG: exact match except reorder of arguments GEOQUERY: same retrieved answer from DB

15 Experimental Evaluation (3)

16 Experimental Evaluation (4)

17 Future Research (1) SCISSOR: more accurate  requires additional human annotation in the form of SAPTs  constructed automatically Domain & corpus  Limited domains  open domain  Constructing large annotated corpus of (NL MR) pairs OntoNotes corpus is assembling currently.

18 Future Research (2) Another way to obtain the requisite supervision  to allow ordinary users themselves to provide the necessary feedback Sentence-meaning pair could be automatically constructed  inferring the meaning of a sentence from the context in which it was uttered

19 Future Research (3) Symbol Grounding Problem (SGP)  Harnad, S. (1990)  Extended from Chinese Room Argument (Searle, 1980) Challenge against Turing Test  the Dictionary-Go-Round (1) Suppose you had to learn Chinese as a second language and the only source of information you had was a Chinese/Chinese dictionary.  The Dictionary-Go-Round (2) -- SGP Suppose you had to learn Chinese as a first language and the only source of information you had was a Chinese/Chinese dictionary! Clearly, a deep understanding of most natural language requires capturing the connection between the abstract concepts underlying words and phrases and their embodiment in the physical world.

20 Thanks!!


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