Adaptive Parsing: Self-Extending Natural Language Interfaces by Jill Fain Lehman

By Jill Fain Lehman

As the pc steadily automates human-oriented initiatives in a number of environ­ ments, the interface among desktops and the ever-wider inhabitants of human clients assumes steadily expanding value. within the workplace surroundings, for example, clerical initiatives equivalent to record submitting and retrieval, and higher-level initiatives resembling scheduling conferences, making plans journey itineraries, and generating records for booklet, are being in part or completely automatic. the diversity of clients for workplace­ orientated software program contains clerks, secretaries, and businesspersons, none of whom are predominantly laptop literate. an identical phenomenon is echoed within the manufacturing facility creation line, within the securities buying and selling ground, in govt companies, in educa­ tional associations, or even in the house. The arcane command languages of definite­ teryear have confirmed too excessive a barrier for gentle popularity of automatic func­ tions into the office, irrespective of how valuable those services can be. computing device­ naive clients easily don't take some time to profit intimidating and complicated computing device interfaces. so one can position the performance of modem pcs on the disposition of various consumer populations, a couple of diverse ways were attempted, many assembly with an important degree of good fortune, to wit: certain classes to coach clients within the less complicated command languages (such as MS-DOS), designing point-and-click menu/graphics interfaces that require less consumer familiarization (illustrated such a lot in actual fact within the Apple Macintosh), and interacting with the consumer in his or her language of choice.

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Considered as a distinct unit (see Chapter 5), Segmentation and the Coalesce/Expand Cycle comprise a bouom-up parser capable of learning new instances of known extendable classes. When we embed the Coalesce/Expand Cycle inside the ParselRecovery Cycle, however, we create a least-deviant-first parser by integrating Error Detection Chapter 4 47 & Recovery into the Expand phase of the bottom-up algorithm (see Chapter 6). Error Detection & Recovery has the dual responsibilities of catching violated expectations in a candidate grammatical constituent (called a context) and augmenting the parse tree with recovery notations.

1. Representing knowledge in CHAMP Considered from the system's point of view, CHAMP performs two tasks. The first is to aid in the duties of an executive assistant to a professor/entrepreneur. These duties consist of helping to maintain a schedule of meetings and events, and helping to arrange airline reservations when events require travel. CHAMP's second and less visible task is to extend its grammar to understand more effectively the idiosyncratic language of a particular user. Because the tasks are distinct, it is useful to distinguish 48 System Architecture and Knowledge Representation between two types of knowledge in CHAMP: the application-related structures that organize the domain concepts and databases, and the learning-related structures that comprise the grammar.

The low values were due largely to the fact that she never managed to find a parsable form for an entire class of subtasks (those involving the change action). Although User 8 did rely on the few forms that 12specifically, she was told to try to type simple, but fully grammatical sentences and to think of the system as someone to whom she was writing instructions. She was the only user who was given this aid. " indicates approximate value due to damage to log ftles "+" indicates instructions to work quickly given this session).

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