document: Web scale NLP : a case study on url word breaking This paper uses the URL word breaking task as an example to elaborate what we identify as crucial in designing statistical natural language processing ( NLP ) algorithms for Web scale applications : ( 1 ) rudimentary multilingual capabilities to cope with the global nature of the Web , ( 2 ) multi-style modeling to handle diverse language styles seen in the Web contents , ( 3 ) fast adaptation to keep pace with the dynamic changes of the Web , ( 4 ) minimal heuristic assumptions for generalizability and robustness , and ( 5 ) possibilities of efficient implementations and minimal manual efforts for processing massive amount of data at a reasonable cost . We first show that the state-of-the-art word breaking techniques can be unified and generalized under the Bayesian minimum risk ( BMR ) framework that , using a Web scale N-gram , can meet the first three requirements . We discuss how the existing techniques can be viewed as introducing additional assumptions to the basic BMR framework , and describe a generic yet efficient implementation called word synchronous beam search . Testing the framework and its implementation on a series of large scale experiments reveals the following . First , the language style used to build the model plays a critical role in the word breaking task , and the most suitable for the URL word breaking task appears to be that of the document title where the best performance is obtained . Models created from other language styles , such as from document body , anchor text , and even queries , exhibit varying degrees of mismatch . Although all styles benefit from increasing modeling power which , in our experiments , corresponds to the use of a higher order N-gram , the gain is most recognizable for the title model . The heuristics proposed by the prior arts do contribute to the word breaking performance for mismatched or less powerful models , but are less effective and , in many cases , lead to poorer performance than the matched model with minimal assumptions . For the matched model based on document titles , an accuracy rate of 97.18 % can already be achieved using simple trigram without any heuristics .
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