Abstract

Automatically extracting quantities and generating final answers for numerical attributes is very useful in many occasions, including question answering, image processing, human-computer interaction, etc. A common approach is to learn linguistics templates or wrappers and employ some algorithm or model to generate a final answer. However, building linguistics templates or wrappers is a tough task for builders. In addition, linguistics templates or wrappers are domain-dependent. To make the builder escape from building linguistics templates or wrappers, we propose a new approach to final answer generation based on Predicates-Units Table (PUT), a mini domain-independent knowledge base. It is deserved to point out that, in the following cases, quantities are not represented well. Quantities are absent of units. Quantities are perhaps wrong for a given question. Even if all of them are represented well, their units are perhaps inconsistent. These cases have a strong impact on final answer solving. One thousand nine hundred twenty-six real queries are employed to test the proposed method, and the experimental results show that the average correctness ratio of our approach is 87.1%.

Highlights

  • Quantity extraction for numerical attribute is very useful in many occasions including question answering [1], image processing [2], human-computer interaction [3], etc

  • Quantity extraction is necessary to final answer solving for a numerical question in question answering

  • Compared to a general entity, the quantity of numerical attributes is likely used as an operand for a complex numerical question

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Summary

Introduction

Quantity extraction for numerical attribute is very useful in many occasions including question answering [1], image processing [2], human-computer interaction [3], etc. Quantity extraction is necessary to final answer solving for a numerical question in question answering. If there is not the most frequent quantity, an alternative approach is to calculate the average value of all quantities as the final answer The final answer is unmeaning because the unit is absent Another example is “What is the weight of a dog?” The quantity set is {(2.0, kg), (2.5, kg), (10.0, kg),. Compared to a general entity, the quantity of numerical attributes is likely used as an operand for a complex numerical question.

Preliminaries
Our Approach
Dataset Collection
Dataset Statistics
Dataset Validation
Related Work
Conclusions and Future Work
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