Question Answ ering Based on Semantic Structures Narayanan Sanda Harabagiu Srini ternational Computer Science Institute Department of Computer Science In Univ Center Street ersity of Texas at Dallas eley CA hardson TX Berk Ric snarayanicsiberkeleyedu sandahltutdallasedu interpretation of the question and generates a p os Abstract sible index in an oline battery of ontologies The The ability to answer complex questions p osed in Natu third step consists of building a scalable and expres ral Language dep ends on the depth of the available sive mo del of actions and events which allows the the inferential mecha semantic representations and nisms they supp ort In this pap er we describ e a QA ar sophisticated reasoning imp osed by QA within com and candidate chitecture where questions are analyzed plex scenarios We emb ed the three forms of seman answers generated by identifying predicate argument tic representations and the inference they enable in structures and semantic frames from the input and a novel exible QA architecture that allows us to p erforming structured probabilistic inference using the evaluate the impact of each new form of semantic extracted relations in the context of a domain and sce information on the accuracy of answering complex nario mo del A novel asp ect of our system is a scal questions able and expressive representation of actions and events The remainder of this pap er is organized as fol based on Co ordinated Probabilistic Relational Mo dels Section we present the semantic knowl lows In CPRM In this pap er we rep ort on the ability of the edge that we extract from questions and answers implemented system to p erform several forms of prob as well as our novel QA architecture In Section abilistic and temp oral inferences to extract answers to we detail our mo del of event structure Section complex questions The results indicate enhanced accu presents the typ es of inference that are asso ciated racy over current stateoftheart QA systems details the results with the event structure Section Intro duction summarizes the of our initial evaluations Section conclusions Curren t Question Answering QA systems extract answers from large text collections by classify Semantic Structures for QA ing the answer typ e they exp ect using question keywords or patterns asso ciated with questions to Pro cessing complex questions involves the identica ranking identify candidate answer passages and tion of several forms of complex semantic structures the candidate answers to decide which passage con First we need to recognize the answer typ e that is Few systems also justify the tains the exact answer exp ected which is a rich semantic structure in the answer by p erforming ab duction in rstorder pred case of a complex question or a mere concept in van et al This paradigm icate logic Moldo the case of a factual question Second we need to is limited by the assumption that the answer can identify the question class or the question pattern b e found b ecause it uses the question words Al Third in the case of a complex question which is though this may happ en sometimes this assump part of a scenario we need to mo del the topic of the tion do es not cover the common case where an in scenario formative answer is missed b ecause its identication At least three forms of information are needed for question classes and requires more sophisticated pro cessing than named detecting the answer typ e named entity classes syntactic dep endency in entity recognition and the identication of an answer formation and semantic information taking the typ e Therefore we argue that access to rich seman form of i predicateargument structures or seman tic structures derived from domain mo dels as well as tic frames and ii the representation of the question from questions and answers enables the retrieval of topic The following question illustrated the signi more accurate answers as well as inference pro cesses cance of each of the three forms of information that explain the validity and contextual coverage of What stimulated Indias missile program Q answers e consider several stages of deep er semantic pro The question stem what is ambiguous as multiple W cessing for answering complex questions A rst answer typ es could b e asso ciated with a question To nd candidate step in this direction is the incorp oration of se pattern What stimulated X mantic parsers that recognize predicateargument answers the recognition of India and other related structures or semantic frames when pro cessing b oth named entities eg Indian as well as the name missile or its related program is im questions and do cuments A second step is the iden of the Prithvi p ortant To b etter pro cess question Q the syntac tication of a topic mo del that contributes to the Question Processing Document Processing Answer Processing Keyword Recognition Indexing and Retrieval based on Candidate lexico−semantic knowledge Answers Syntactic Parse Named Entity Recognition Identification of Identification of Recognition of Probabilistic Inference Complex Topic Model Frame Structures Event Structure Question Network Identification of Identification of Recognition Predicate−Argument Recognition of Syntactic Parse of Answer Structures Frame Structures Answer Type Named Entity Recognition Structure Documents Ontologies Answer Figure QA architecture based on several forms of semantic structures tic dep endencies enable the recognition of predicate Question PATTERN: How can X be detected? t structures The predicateargument struc argumen Question FOCUS: X = biological weapons program ture of Q is TOPIC MODEL PREDICATE: Stimulate Topic relations: [develop −− program], [produce −− bilogical agents] ARG0 (role = agent) ANSWER (part 1) ARG1 (role = thing increasing): India’s missile progam [stockpile −− weapons], [deliver −− missiles] ARG2 (role = instrument) : ANSWER (part 2) Possible paths of action 1) development −−> production −−> stockpiling −−> delivery predicateargument structure was built based The 2) development −−> acquisition −−> stockpiling −−> delivery pro ject Kings on the denitions of the PropBank et al The structure indicates that the bury Predicate−argument structure er may have the role of agent or even the role answ PREDICATE: = detect Arg0 (detector) : Answer (1) instrument When additional information from of Arg1 (detected): biological weapons program Arg2 (instrument) ; Answer (2) Baker et al is used we nd that FrameNet the answer may have four other semantic roles de FOCUS Interpretation ed as frame elements of two distinct frames riv 1) program for producing biological weapons FRAME: Stimulate 2) program for acquiring biological weapons Frame element CIRCUMSTANCES: ANSWER (part 1) Frame Element EXPERIENCER: India’s missile progam PREDICATE: = produce PREDICATE: = acquire Frame Element STIMULUS : ANSWER (part 2) Arg0 (producer) : Answer Arg0 (buyer) : Answer Arg1 (product): biological weapons Arg1 (object): biological weapons FRAME: Subject_stimulus Frame element CIRCUMSTANCES: ANSWER (part 3) Question pro cessing based on topic mo dels Frame element COMPARISON SET: ANSWER (part 4) Figure Frame element EXPERIENCER: India’s missile program Frame element PARAMETER: nuclear proliferation inal predicateargument structure in other predicate None of these semantic roles are fully sp ecied structures in which the semantic typ e of the answer To interpret the semantic information constrained has less ambiguity Figure illustrates the mapping by the thematic roles we need to also have access to of the predicate detect in the predicates produce and a topic model of the scenario in which the question e that can b e extracted in parallel This map acquir is b eing asked For example for the question Q ping enabled by the topic mo del corresp onds to the How can a biological weapons program be detected decomp osition of the original complex questions into the topic mo del consists of a a set of typical a set of less complex questions relations b etween topic concepts and b a set of Because the mo del for event structure has the ca p ossible paths of actions As it is illustrated in Fig pability of incorp orating domain knowledge in ure the identication of a predicateargument O WLbased representations and p erforms sev structures and b semantic frames contributes to eral forms on inference on this knowledge it can b e the recognition of the exp ected answer as well as to used to extract candidate answers from the passages the formation of the topic mo del retrieved by the topic relations The QA architec Question Q is mapp ed into its pattern and its ture that takes advantage of these semantic struc cus which has the role of the topic of the ques fo tures and the inference they enable is illustrated tion The do cument passages retrieved for the sp e in Figure The syntactic parse is pro duced by cic topic can b e used to extract the most relevant the Collins parser Collins the Named En topic relations with the metho d detailed in Section tity Recognizer NER is an implementation of the The event structure detailed in Section enables NER rep orted in Bikel et al whereas the the recognition of p ossible paths of action in the format of chains b etween the events lexicalized in 1 OWL is a markup language for the semantic web the topic relations The set of p ossible paths of ac httpwwwsemanticweborg which allows for the sp eci tions generate dierent interpretations of the ques cation of ontologies and the semantic markup of do cuments format on the web in an