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Semantics-based Graph Approach to
Complex Question-Answering
Tomasz Jurczyk and Jinho D. Choi
Department of Mathematics and Computer Science, Emory University
ARITHMETIC QUESTIONS
We gratefully acknowledge a grant from the National Science
Foundation that has supported our travel and the presentation of
this work.
ACKNOWLEDGEMENT
• Question answering has lately gained lots of interest in
academic research as it allows to seek for answers.
• Output of current tools provide syntactic and semantic
structures of text must be post-processed to anwer questions
• Robustness of handling several types of questions
Example of our semantic-based graph
• Increasngly important to explore possible approaches of
representing knowledge for complex question-answering
• Designed semantics-based graph to represent entity relations
Attribute types
• Arithmetic questions chosen as one of the applications for
complex question amswering.
• Simple addition and subtraction level from elementary level.
• Mostly concerns contiguous representation of state changes.
The following features are used in statistical model for
annotating verb polarities:
• Semantic role labels (numbered arguments as in PropBank
(Palmer et al., 2005).
• Sequence of verbs and arguments, whose semantic roles are
recognized as ‘themes’ in question.
• Frequency of verbs and theme arguments in the context.
• Similarity between verbs and theme arguments across all
sentences in one document (question).
• Distance of the verb to the final question.
• 395 questions together with their equations and answers.
• Divided into 3 folds with similar distribution of polarity verbs
across different sets.
• Cross validation shows accuracy score of 71.75%.
Error analysis shown that the majority of errors were caused by:
• Syntactic and semantic parsing errors, (verb not properly
recognized or semantic role labels incorrectly assigned)
• Coreference resolution errors (‘he’ or ‘she’ not recognized).
Sample of arithmetic questions
Crucial parts in solving arithmetic questions:
• Typically, a question concerns the start state, the transition
state, or the end state of a specific theme (e.g., pizza, kitten).
• Coreference mentions play an important role in answering
these questions due to the fact that questions generally
consists of multiple sentences
• Sequence classification of verb polarities, where each verb
in a sentence is categorized as ‘+’, ‘-’ or ‘0’.
• Once all verbs are categorized, equation is being formed and
simple algebra problem has to be solved.
Flow of execution
Example flow of execution
Distributions and accuracies of all folds
• Proposed semantics-based knowledge approach for complex
question answering applied on arithmetic questions.
• By extracting appropriate features and building a statistical
model our system showed promising results.
• In the future, we plan to work on exploring new features and
applying our approach on other types of arithmetic questions
(e.g.: multiplication) and other types of complex questions.
CONCLUSIONS
• Graph consists of documents, entities, instances, attributes
• Documents group text into document-segmented tex
• Entities represent coreference resolution information
• Relations between instances and attributes are based on
syntactic and semantic labels
• Currently, graph is being constructed deterministically and is
represented as rule based algorithm
OVERVIEW EXPERIMENTS

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Semantics-based Graph Approach to Complex Question-Answering

  • 1. Semantics-based Graph Approach to Complex Question-Answering Tomasz Jurczyk and Jinho D. Choi Department of Mathematics and Computer Science, Emory University ARITHMETIC QUESTIONS We gratefully acknowledge a grant from the National Science Foundation that has supported our travel and the presentation of this work. ACKNOWLEDGEMENT • Question answering has lately gained lots of interest in academic research as it allows to seek for answers. • Output of current tools provide syntactic and semantic structures of text must be post-processed to anwer questions • Robustness of handling several types of questions Example of our semantic-based graph • Increasngly important to explore possible approaches of representing knowledge for complex question-answering • Designed semantics-based graph to represent entity relations Attribute types • Arithmetic questions chosen as one of the applications for complex question amswering. • Simple addition and subtraction level from elementary level. • Mostly concerns contiguous representation of state changes. The following features are used in statistical model for annotating verb polarities: • Semantic role labels (numbered arguments as in PropBank (Palmer et al., 2005). • Sequence of verbs and arguments, whose semantic roles are recognized as ‘themes’ in question. • Frequency of verbs and theme arguments in the context. • Similarity between verbs and theme arguments across all sentences in one document (question). • Distance of the verb to the final question. • 395 questions together with their equations and answers. • Divided into 3 folds with similar distribution of polarity verbs across different sets. • Cross validation shows accuracy score of 71.75%. Error analysis shown that the majority of errors were caused by: • Syntactic and semantic parsing errors, (verb not properly recognized or semantic role labels incorrectly assigned) • Coreference resolution errors (‘he’ or ‘she’ not recognized). Sample of arithmetic questions Crucial parts in solving arithmetic questions: • Typically, a question concerns the start state, the transition state, or the end state of a specific theme (e.g., pizza, kitten). • Coreference mentions play an important role in answering these questions due to the fact that questions generally consists of multiple sentences • Sequence classification of verb polarities, where each verb in a sentence is categorized as ‘+’, ‘-’ or ‘0’. • Once all verbs are categorized, equation is being formed and simple algebra problem has to be solved. Flow of execution Example flow of execution Distributions and accuracies of all folds • Proposed semantics-based knowledge approach for complex question answering applied on arithmetic questions. • By extracting appropriate features and building a statistical model our system showed promising results. • In the future, we plan to work on exploring new features and applying our approach on other types of arithmetic questions (e.g.: multiplication) and other types of complex questions. CONCLUSIONS • Graph consists of documents, entities, instances, attributes • Documents group text into document-segmented tex • Entities represent coreference resolution information • Relations between instances and attributes are based on syntactic and semantic labels • Currently, graph is being constructed deterministically and is represented as rule based algorithm OVERVIEW EXPERIMENTS