Download An introduction to language processing with Perl and Prolog by Pierre M. Nugues PDF

By Pierre M. Nugues

This publication teaches the foundations of usual language processing and covers linguistics matters. It additionally information the language-processing services concerned, together with part-of-speech tagging utilizing ideas and stochastic innovations. A key function of the e-book is the author's hands-on method all through, with wide workouts, pattern code in Prolog and Perl, and a close advent to Prolog. The booklet is appropriate for researchers and scholars of typical language processing and computational linguistics.

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Extra info for An introduction to language processing with Perl and Prolog : an outline of theories, implementation, and application with special consideration of English, French, and German

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Character classes are useful to search patterns with spelling differences, such as [Cc]omputer [Ss]cience, which matches four different strings: Computer Science Computer science computer Science computer science We can define the complement of a character class, that is, the characters of the alphabet that are not member of the class, using the caret symbol, ˆ, as the first symbol inside the angle brackets. [ˆa] means any character that is not an a. [ˆ0123456789] means any character that is not a digit.

Speech input Whisper Speech recognition Names database Names Proper noun substitution Action templates database NLP Language analysis Semantic Template matching Object description CDs database Player/Reactor Animation engine Video output Animated parrot Speech and animation database Speech controller Sound output Jukebox Dialogue Context and conversation state Dialogue rules database Application CD changer Fig. 10. Architecture of the Persona conversational assistant. After Ball et al. (1997). 2 The Persona’s Modules Persona’s first component is the Whisper speech recognition module (Huang et al.

The corpus is usually collected through fake dialogues between a real user and a person simulating the machine answers. Repeating such experiments with a reasonable number of users enables us to acquire a text set covering what the machine can expect from potential users. It is then easier to determine the vocabulary of an application, to have a precise idea of word frequencies, and to know the average length of sentences. In addition, the dialogue corpus enables the analyst to understand what the user expects from the machine, that is, how s/he interacts with it.

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