How does nlP relate to text mining? Text mining uses natural language processing to induce structure into the text collection and then uses data mining algorithms such as classification, clustering association, and sequence discovery to extract knowledge from it 3. What are some of the benefits and challenges of NLP? NLP moves beyond syntax-driven text manipulation(which is often called word counting)to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context. The challenges include Part-of-speech tagging. It is difficult to mark up terms in a text as corresponding to a particular part of speech because the part of speech depends not only on the definition of the term but also on the context within which it is used Text segmentation. Some written languages, such as Chinese, Japanese and Thai, do not have single-word boundaries Word sense disambiguation. Many words have more than one meaning Selecting the meaning that makes the most sense can only be accomplished by taking into account the context within which the word is used Syntactic ambiguity. The grammar for natural languages is ambiguous; that is, multiple possible sentence structures often need to be considered Choosing the most appropriate structure usually requires a fusion of semantic and contextual information Imperfect or irregular input. Foreign or regional accents and vocal imped iments in speech and typographical or gramma ical errors in texts make the processing of the language an even more difficult task Speech acts. A sentence can often be considered an action by the speake The sentence structure alone may not contain enough information to define this action 4. What are the most common tasks addressed by Following are among the most popular tasks ering Automatic summarization Natural language generation Copyright o201& Pearson Education, Inc.5 Copyright © 2018Pearson Education, Inc. 2. How does NLP relate to text mining? Text mining uses natural language processing to induce structure into the text collection and then uses data mining algorithms such as classification, clustering, association, and sequence discovery to extract knowledge from it. 3. What are some of the benefits and challenges of NLP? NLP moves beyond syntax-driven text manipulation (which is often called “word counting”) to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context. The challenges include: • Part-of-speech tagging. It is difficult to mark up terms in a text as corresponding to a particular part of speech because the part of speech depends not only on the definition of the term but also on the context within which it is used. • Text segmentation. Some written languages, such as Chinese, Japanese, and Thai, do not have single-word boundaries. • Word sense disambiguation. Many words have more than one meaning. Selecting the meaning that makes the most sense can only be accomplished by taking into account the context within which the word is used. • Syntactic ambiguity. The grammar for natural languages is ambiguous; that is, multiple possible sentence structures often need to be considered. Choosing the most appropriate structure usually requires a fusion of semantic and contextual information. • Imperfect or irregular input. Foreign or regional accents and vocal impediments in speech and typographical or grammatical errors in texts make the processing of the language an even more difficult task. • Speech acts. A sentence can often be considered an action by the speaker. The sentence structure alone may not contain enough information to define this action. 4. What are the most common tasks addressed by NLP? Following are among the most popular tasks: • Question answering • Automatic summarization • Natural language generation