By Nitin Indurkhya, Fred J. Damerau
The guide of traditional Language Processing, moment version provides useful instruments and methods for enforcing usual language processing in computers. in addition to elimination outmoded fabric, this version updates each bankruptcy and expands the content material to incorporate rising components, resembling sentiment research. New to the second one variation higher prominence of statistical ways New purposes part Broader multilingual scope to incorporate Asian and eu languages, in addition to English An actively maintained wiki (http://handbookofnlp.cse.unsw.edu.au) that offers on-line assets, supplementary info, and updated advancements Divided into 3 sections, the publication first surveys classical concepts, together with either symbolic and empirical methods. the second one part makes a speciality of statistical ways in typical language processing. within the ultimate part of the e-book, each one bankruptcy describes a specific classification of software, from chinese language laptop translation to details visualization to ontology building to biomedical textual content mining. totally up to date with the newest advancements within the box, this accomplished, sleek guide emphasizes find out how to enforce functional language processing instruments in computational structures.
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Additional info for Handbook of Natural Language Processing, Second Edition (Chapman & Hall Crc: Machine Learning & Pattern Recognition)
Example text
The specific cases vary from one language to the next, and the specific treatment of the punctuation characters needs to be enumerated within the tokenizer for each language. In this section, we give examples of English tokenization. Abbreviations are used in written language to denote the shortened form of a word. In many cases, abbreviations are written as a sequence of characters terminated with a period. When an abbreviation occurs at the end of a sentence, a single period marks both the abbreviation and the sentence boundary.
Various modules in the pipeline attempt to classify all instances of punctuation marks by identifying periods in numbers, date and time expressions, and abbreviations. The preprocess utilizes a list of 75 abbreviations and a series of over 100 hand-crafted rules and was developed over the course of more than six staff months. 1%) on a large Wall Street Journal corpus. However, the performance was improved when integrated with the trainable system Satz, described in Palmer and Hearst (1997), and summarized later in this chapter.
For languages with a unique alphabet not used by any other languages, such as Greek or Hebrew, language identification is determined by character set identification. Similarly, character set identification can be used to narrow the task of language identification to a smaller number of languages that all share many characters, such as Arabic vs. Persian, Russian vs. Ukrainian, or Norwegian vs. Swedish. The byte range distribution used to determine character set identification can further be used to identify bytes, and thus characters, that are predominant in one of the remaining candidate languages, if the languages do not share exactly the same characters.