: ;Contents: Computer systems for natural language; SHRDLU, A system for dialog; Representation, formalisms for knowledge; Frames, some ideas for a new formalism; Conceptual programming, applying artificial intelligence to program writing.

https://onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog0101_2

Five Lectures on Artificial Intelligence

An overview of KRL, a Knowledge Representation Language

The TREC-8 Question Answering (QA) Track was the first large-scale evaluation of domain-independent question answering systems. In addition to fostering research on the QA task, the track was used to investigate whether the evaluation methodology used for document retrieval is appropriate for a different natural language processing task. As with document relevance judging, assessors had legitimate differences of opinions as to whether a response actually answers a question, but comparative evaluation of QA systems was stable despite these differences. Creating a reusable QA test collection is fundamentally more difficult than creating a document retrieval test collection since the QA task has no equivalent to document identifiers.

background or methods

/pdf/building-a-question-answering-test-collection-3cucppyj85.pdf

Building a question answering test collection

An information server directs users of the information server to desired sources of information where the desired sources of information are determined, at least in part, based on user input. The information server includes a query input processor, a question processor and an answer processor. The query input processor is used for accepting an initial user query. The question processor processes the initial user query to identify a set of possible well-formed questions selected from the question database, where a well-formed question is a question in the database that is coupled to at least one answer reference. The answer reference is typically either an answer or a pointer to a possible location of an answer. In a specific embodiment, the information server is coupled to the Internet so that users can pose questions using a Web browser from any Internet-connected device. In some systems, the question processor includes a tokenizer for tokenizing the initial user query into a list of words, a parser for generating a syntactic structure from the list of words, a normalizer for reducing the syntactic structure to a canonical syntactic structure, and a matcher for matching the canonical syntactic structure against a semantic network to obtain a weighted list of well-formed questions representative of possible semantic meanings for the initial user query.

Grammar template query system

A language and a program for stating and solving combinatorial problems

In this paper, we study the answer sentence selection problem for question answering. Unlike previous work, which primarily leverages syntactic analysis through dependency tree matching, we focus on improving the performance using models of lexical semantic resources. Experiments show that our systems can be consistently and significantly improved with rich lexical semantic information, regardless of the choice of learning algorithms. When evaluated on a benchmark dataset, the MAP and MRR scores are increased by 8 to 10 points, compared to one of our baseline systems using only surface-form matching. Moreover, our best system also outperforms pervious work that makes use of the dependency tree structure by a wide margin.

background

/pdf/question-answering-using-enhanced-lexical-semantic-models-2to6agzn0r.pdf

Question Answering Using Enhanced Lexical Semantic Models

/pdf/five-lectures-on-artificial-intelligence-4jw2skek3c.pdf

Five Lectures on Artificial Intelligence

Citations

An overview of KRL, a Knowledge Representation Language

Building a question answering test collection

Grammar template query system

A language and a program for stating and solving combinatorial problems

Question Answering Using Enhanced Lexical Semantic Models

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