Download e-book for iPad: Advances in Open Domain Question Answering (Text, Speech and by Tomek Strzalkowski, Sanda Harabagiu

By Tomek Strzalkowski, Sanda Harabagiu

ISBN-10: 1402047444

ISBN-13: 9781402047442

ISBN-10: 1402047460

ISBN-13: 9781402047466

This new Springer quantity offers a complete and designated examine present methods to computerized query answering. the extent of presentation is appropriate for novices to the sphere in addition to for execs wishing to check this region and/or to construct functional QA platforms. The e-book can function a "how-to" instruction manual for IT practitioners and procedure builders. it will probably even be used to coach graduate classes in computing device technology, details technological know-how and comparable disciplines.

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Extra resources for Advances in Open Domain Question Answering (Text, Speech and Language Technology)

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The remaining of this section describes each of the introduced modules in detail. 2 Detection of Multimedia Question Types The detection of multimedia questions translates into an analysis of which answer types might accept multimedia answers. We were surprised to realize that most answer types can be mapped to images, which is a further indication about the utility of multimedia QA systems. In fact, the only answer types in our ontology that are not relevant for a multimedia QA system are numerical answer types such as: NUMBER, QUANTITY, DATE, TIME, PRICE, and PERCENT.

This filter discards images whose name contains specific strings we identified as typical layout image names. The most common sub-strings we discovered are: “dot”, “arrow”, “null ”, “pixel”, “spacer”, “clear”, “blank”, “bullet”, “logo”, “banner”, “icon”, “bar” and “button”. 3. The images that passed the previous filter are stored in the normalized document, preserving the relative document position. Only these images are considered as candidate multimedia answers. 30 D. MOLDOVAN, M. PAŞCA AND M.

During the relaxation, arguments to predicates in the question are incrementally uncoupled, the proof score is reduced, and the justification is re-attempted. The loop between the Justification and the Relaxation modules continues until the proof succeeds, or the proof score is below a predefined threshold. When all the candidate answers are processed, the candidate answers are ranked based on their proof scores, with the output from COGEX being the ranked answers and the answer justifications.

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Advances in Open Domain Question Answering (Text, Speech and Language Technology) by Tomek Strzalkowski, Sanda Harabagiu


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