MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

Open-domain question answering

Question answering aims to develop techniques that can go beyond the retrieval of relevant documents in order to return exact answers to natural language questions, such as “How tall is the Eiffel Tower?”, “Which cities have a subway system?”, and “Who is Alberto Tomba?”. Answering natural language questions requires more complex processing of text than employed by current information retrieval...

Open-Domain Question-Answering

Linguistically Informed Question Answering

ion over Parts of Speech. Lexical units are grouped into frames irrespective of their parts of speech. This allows to easily map, e. g., two text fragments onto each other that carry essentially the same meaning, but where one is headed by a verb and the other by a noun, such as ‘A bought B’ vs. ‘(the) acquisition of B by A’. In GermaNet, this mapping requires additional knowledge in the form o...

Open-domain textual question answering techniques

Textual question answering is a technique of extracting a sentence or text snippet from a document or document collection that responds directly to a query. Open-domain textual question answering presupposes that questions are natural and unrestricted with respect to topic. The question answering (Q/A) techniques, as embodied in today’s systems, can be roughly divided into two types: (1) techni...

Experiments with Open-Domain Textual Question Answering

This paper describes the integration of several knowledge-based natural language processing techniques into a Question Answering system, capable of mining textual answers from large collections of texts. Surprizing quality is achieved when several lightweight knowledge-based NLP techniques comI)lement mostly shallow, surface-based approaclms.