Question Answering - Issues

Issues

In 2002 a group of researchers wrote a roadmap of research in question answering. The following issues were identified.

Question classes

Different types of questions (e.g., "What is the capital of Liechtenstein?" vs. "Why does a rainbow form?" vs. "Did Marilyn Monroe and Cary Grant ever appear in a movie together?") require the use of different strategies to find the answer. Question classes are arranged hierarchically in taxonomies.

Question processing

The same information request can be expressed in various ways, some interrogative ("Who is the King of Lesotho?") and some assertive ("Tell me the name of the King of Lesotho."). A semantic model of question understanding and processing would recognize equivalent questions, regardless of how they are presented. This model would enable the translation of a complex question into a series of simpler questions, would identify ambiguities and treat them in context or by interactive clarification.

Context and QA

Questions are usually asked within a context and answers are provided within that specific context. The context can be used to clarify a question, resolve ambiguities or keep track of an investigation performed through a series of questions. (For example, the question, "Why did Joe Biden visit Iraq in January 2010?" might be asking why Vice President Biden visited and not President Obama, why he went to Iraq and not Afghanistan or some other country, why he went in January 2010 and not before or after, or what Biden was hoping to accomplish with his visit. If the question is one of a series of related questions, the previous questions and their answers might shed light on the questioner's intent.)

Data sources for QA

Before a question can be answered, it must be known what knowledge sources are available and relevant. If the answer to a question is not present in the data sources, no matter how well the question processing, information retrieval and answer extraction is performed, a correct result will not be obtained.

Answer extraction

Answer extraction depends on the complexity of the question, on the answer type provided by question processing, on the actual data where the answer is searched, on the search method and on the question focus and context.

Answer formulation

The result of a QA system should be presented in a way as natural as possible. In some cases, simple extraction is sufficient. For example, when the question classification indicates that the answer type is a name (of a person, organization, shop or disease, etc.), a quantity (monetary value, length, size, distance, etc.) or a date (e.g. the answer to the question, "On what day did Christmas fall in 1989?") the extraction of a single datum is sufficient. For other cases, the presentation of the answer may require the use of fusion techniques that combine the partial answers from multiple documents.

Real time question answering

There is need for developing Q&A systems that are capable of extracting answers from large data sets in several seconds, regardless of the complexity of the question, the size and multitude of the data sources or the ambiguity of the question.

Multilingual (or cross-lingual) question answering

The ability to answer a question posed in one language using an answer corpus in another language (or even several). This allows users to consult information that they cannot use directly. (See also Machine translation.)

Interactive QA

It is often the case that the information need is not well captured by a QA system, as the question processing part may fail to classify properly the question or the information needed for extracting and generating the answer is not easily retrieved. In such cases, the questioner might want not only to reformulate the question, but to have a dialogue with the system. (For example, the system might ask for a clarification of what sense a word is being used, or what type of information is being asked for.)

Advanced reasoning for QA

More sophisticated questioners expect answers that are outside the scope of written texts or structured databases. To upgrade a QA system with such capabilities, it would be necessary to integrate reasoning components operating on a variety of knowledge bases, encoding world knowledge and common-sense reasoning mechanisms, as well as knowledge specific to a variety of domains.

Information clustering for QA

Information clustering for question answering systems is a new trend that is originated to increase the accuracy of question answering systems through search space reduction. In recent years this is widely researched through development of question answering systems which support information clustering in their basic flow of process .

User profiling for QA

The user profile captures data about the questioner, comprising context data, domain of interest, reasoning schemes frequently used by the questioner, common ground established within different dialogues between the system and the user, and so forth. The profile may be represented as a predefined template, where each template slot represents a different profile feature. Profile templates may be nested one within another.