up thesaurus

A few months ago, on the internet to find a Chinese thesaurus material (hundreds of k), then want to write a word segmentation program. I do not have any research on Chinese participle, also on the basis of their own imagination to write. If there are relevant experts, please give more advice. One, the word storehouse Thesaurus has about 50,000 words (Google can search, similar

Sensitive words, text filtering is an essential function of a website, how to design a good, efficient filtering algorithm is very necessary. Some time ago I a friend (immediately graduated, contact programming soon) want me to help him to see a text filter thing, it says retrieval efficiency is very slow. I took it to the program to see the whole process is as follows: Read the sensitive thesaurus, if the HashSet collection, get the page upload text,

Part2 word processingAfter installing the related software package in Rstudio, we can do the related word processing, please refer to the Part1 section to install the required package. Reference Document: Play text mining, this article is about using R to do text mining is very detailed, and some related information download, it is worth seeing!1. rwordseg functionThe documentation is available for download at http://download.csdn.net/detail/cl1143015961/8436741 and is simply described here.Word

There is a group of non-daily English vocabulary, I need to calculate in English articles in the most frequent frequency. So I initially thought of traversing the array, using Substr_count to sequentially calculate the number of occurrences of each word, but this would result in multiple repetitions of the entire article scan. Or the article is broken into words, from the array function to calculate the number of intersections, but still feel not ideal. Do you have any ideas? This app is actua

in both the index and query processes to identify exceptions to the noise word dictionaries. A word such as "ATT," for example, will never be indexed by default because the word breaks it into single noise words. to avoid this, the user can add "ATT" to the custom dictionary file; as result, this word will be treated as an exception by the word breaker and will be indexed and queried. these files contain a simple list of words, one per line. if the custom dictionary file is changed, you must pe

banquet development, before called PHPCWS. PHPCWS First Use the API "Ictclas 3.0 share version Chinese Word segmentation algorithm" for the first word processing, and then use the "reverse Maximum matching algorithm" to the word segmentation and Word merge processing, and add punctuation filtering function, get word segmentation results. Unfortunately, only Linux systems are currently supported and have not been ported to the win platform.2, the extraction results are compared with the existing

---Baidu Chinese character intelligent recognition ability big conjecture Baidu has such a smart, naturally because they have a strong background program at work, according to the above five point embodiment way, I guess Baidu backstage at least four functional modules at work, they have their own functions, so that Baidu users have a very good user experience. Below Yi Yilai to talk about my conjecture. A synonym (synonymous) Thesaurus: must have a

contents inside if newer copy to the output directoryPart II: Adding references to Lucene.net.dll files and PanGu.Lucene.Analyzer.dll filesAnalyzer Analyzer = new Lucene.Net.Analysis.PanGu.PanGuAnalyzer ();Tokenstream Tokenstream = Analyzer. Tokenstream ("", New System.IO.StringReader ("Beijing, hi welcome you All"));Lucene.Net.Analysis.Token Token = null;while (token = Tokenstream.next ()) = null){LISTBOX1.ITEMS.ADD (token. Termtext ());}Since it is participle, there must be a

allowed to participle. In contrast with StandardAnalyzer and Chineseanalyzer indexing time is similar, index file size is similar, cjkanalyzer performance will be worse, index file large and time-consuming longer.To solve the problem, first analyze the three parser of the word breaker. StandardAnalyzer and Chineseanalyzer are the sentences in a single word, that is, "milk is not as good as juice," they will be cut into "milk is not as good as juice", and Cjkanalyzer will be cut into "cow grandm

will be cut into "milk is not as good as juice", and Cjkanalyzer will be cut into "cow grandma, if the juice is good to drink." 。 This also explains why the search for "juice" can match this sentence.There are at least two drawbacks to the above participle: mismatched matching and large index file. Our goal is to break down the above sentences into "milk is not as good as juice." The key here is semantic recognition, how do we recognize that "milk" is a word and "milk not" is not a word? We wil

improperly handles special characters.For example: When searching for the user "sea" Why large v users "kkw in the eyes of the De star Sea" in the "Looking for that sea" behind?For the customers who want to build app search, from the technical level, the implementation of the following scenarios. The cloud Search service is based on Elasticsearch and is able to complete terabytes of retrieval tasks and return results in milliseconds, which can be a good solution to the performance problems of t