خدمة تلخيص النصوص العربية أونلاين،قم بتلخيص نصوصك بضغطة واحدة من خلال هذه الخدمة
Knowledge in Speech and Language Processing
What distinguishes language processing applications from other data processing sys- tems is their use of knowledge of language. Consider the Unix wc program, which is used to count the total number of bytes, words, and lines in a text file. When used to count bytes and lines, wc is an ordinary data processing application. However, when it is used to count the words in a file it requires knowledge about what it means to be a word, and thus becomes a language processing system.
Of course, wc is an extremely simple system with an extremely limited and im- poverished knowledge of language. Sophisticated conversational agents like HAL, or machine translation systems, or robust question-answering systems, require much broader and deeper knowledge of language. To get a feeling for the scope and kind of required knowledge, consider some of what HAL would need to know to engage in the dialogue that begins this chapter, or for a question answering system to answer one of the questions above.
HAL must be able to recognize words from an audio signal and to generate an audio signal from a sequence of words. These tasks of speech recognition and speech synthesis tasks require knowledge about phonetics and phonology; how words are pronounced in terms of sequences of sounds, and how each of these sounds is realized acoustically.
Note also that unlike Star Trek’s Commander Data, HAL is capable of producing contractions like I’m and can’t. Producing and recognizing these and other variations of individual words (e.g., recognizing that doors is plural) requires knowledge about morphology, the way words break down into component parts that carry meanings like singular versus plural.
Moving beyond individual words, HAL must use structural knowledge to properly string together the words that constitute its response. For example, HAL must know
Knowledge in Speech and Language Processing
What distinguishes language processing applications from other data processing sys- tems is their use of knowledge of language. Consider the Unix wc program, which is used to count the total number of bytes, words, and lines in a text file. When used to count bytes and lines, wc is an ordinary data processing application. However, when it is used to count the words in a file it requires knowledge about what it means to be a word, and thus becomes a language processing system.
Of course, wc is an extremely simple system with an extremely limited and im- poverished knowledge of language. Sophisticated conversational agents like HAL, or machine translation systems, or robust question-answering systems, require much broader and deeper knowledge of language. To get a feeling for the scope and kind of required knowledge, consider some of what HAL would need to know to engage in the dialogue that begins this chapter, or for a question answering system to answer one of the questions above.
HAL must be able to recognize words from an audio signal and to generate an audio signal from a sequence of words. These tasks of speech recognition and speech synthesis tasks require knowledge about phonetics and phonology; how words are pronounced in terms of sequences of sounds, and how each of these sounds is realized acoustically.
Note also that unlike Star Trek’s Commander Data, HAL is capable of producing contractions like I’m and can’t. Producing and recognizing these and other variations of individual words (e.g., recognizing that doors is plural) requires knowledge about morphology, the way words break down into component parts that carry meanings like singular versus plural.
Moving beyond individual words, HAL must use structural knowledge to properly string together the words that constitute its response. For example, HAL must know
تلخيص النصوص العربية والإنجليزية اليا باستخدام الخوارزميات الإحصائية وترتيب وأهمية الجمل في النص
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