I would like to tokenize html (not parse!) and javascript so that I can search the sourcecode we produce.
For example, a query of:
field:"jquery.1.11.min.js"
would return documents containing that.
Does anyone have information regarding a code tokenizer?
I'm not aware of any code analyzers, per se. Generally, if you want to index web content, you'dd use some other library to parse it and extract content, then analyze and index the extracted content.
But, looking for the shotgun approach, just chucking a bunch of raw code right into the index. Analysis won't be perfect no matter how you go about it, but it's a approximate effort anyway. I'dd probably go with PatternAnalyzer, for a first pass. I wouldn't even change the defaults. The default, \W+, means your tokens will be consecutive sequences of letters, numbers and underscores, which is about what is generally used for identifiers.
So, if you have:
<script src="jquery-1.11.min.js"></script>
You wind up with tokens like: script, src, jquery, 1, 11, min, js, script
This would be most useful for searching phrases, probably. So, for the search you indicated, you'll be using the same analysis, and be searching for a phrase of five consecutive terms: jquery, 1, 11, min, js. Which seems reasonable enough.
There are some significant weaknesses, of course. It would be impossible to differentiate between 2.11, 2*11 and 2+11, for instance. Something to bear in mind.
Related
How to index mixed language contents in Elasticsearch. Let's say that we have a system where people submit contents from various parts of the world. Countries ranges from US, Canada, Europe, Japan, Korea, India, China, Kenya, Arabs, Russia to all other parts of the world.
Contents can be in any language that we can't know beforehand and can even be in mixed language. We don't want to guess the language of the contents and create multiple language specific indexes for each of the inputted language, we believe this is unmanageable.
We need an easy solution to index those contents efficiently in Elasticsearch with full text search capability as well as fuzzy string searching. Can anyone help in this regard?
What is the target you want to achieve? Do you want to have hits only in the language used at query time? Or would you also accept hits in any other language?
One approach would be to run all of elasticsearch's different language analyzers on the input and store the result in separate fields, for instance suffixed by the language of the current analyzer.
Then, at query time, you would have to search in all of these fields if you have no method to guess the most relevant ones.
However, this is likely to explode since you create a multitude of unused duplicates. This is IMHO also less elegant than having separate indices.
I would strongly recommend to evaluate if you really do not know the number of languages you will see during production. Having a distinct index per language would give you much more control over the input/output and enable you to fine tune your engine to the actual use case.
Alternatively, you may start with a simple whitespace tokenizer and evaluate the quality of the search results (per use case).
You will not have language specific stemming but at least token streams for most languages.
I'm currently developing a website that allows a search on a PostgreSQL
database, the search works with to_tsquery() and I'm trying to find a way to validate the input before it's being sent as a query.
Other than that I'm also trying to add a phrasing capability, so that if someone searches for HELLO | "I LIKE CATS" it will only find results with "hello" or the entire phrase "i like cats" (as opposed to I & LIKE & CATS that will find you articles that have all 3 words,
regardless where they might appear).
Is there some reason why it's too expensive to let the DB server validate it? It does seem a bit excessive to duplicate the ts_query parsing algorithm in the client.
If the concern is that you don't want it to try running the whole query (which presumably will involve table access) each time it validates, you could use the input in a smaller query, just in pseudocode (which may look a bit like Python, but that's just coincidence):
is_valid_query(input):
try:
execute("SELECT ts_query($1)", input);
return True
except DatabaseError:
return False
With regard to phrasing, it's probably easiest to search by the non-phrased query first (using indexes), then filter those for having the phrase. That could be done server side or client side. Depending on the language being parsed, it might be easiest to construct a simple regex of the phrase that deals with repeated whitespace or other ignorable symbols.
Search for to_tsquery('HELLO|(I&LIKE&CATS)'), getting back a list of documents which loosely match.
In the client, filter that to those matching the regex "HELLO|(I\s+LIKE\s+CATS)".
The downside is you do need some additional code for translating your query into the appropriate looser query, and then for translating it into a regex.
Finally, there might be a technique in PostgreSQL to do proper phrase searching using the lexeme positions that are stored in ts_vectors. I'm guessing that phrase searches are one of the intended uses, but I couldn't find an example of it in my cursory search. There's a section on it near the bottom of http://linuxgazette.net/164/sephton.html at least.
I have a huge amount of documents (mainly pdfs and doc's) I want to classify, so I can search over them according to certain tags. These tags could either be of my own (I put the tags to the document) or extracted from the text.
I've just seen a post related to this (Classify data using Apache Mahout), but perhaps there is something even more simple.
Mahout might be overkill for your problem - but you can get a fairly quick, easy solution by using OpenNLP.
http://opennlp.sourceforge.net/api/index.html
Specifically, look at the opennlp.tools.doccat package. Essentially, you have to go through and manually tag a small(ish) set of the items for each category you desire. If they are really distinct, you can get away with a small sample size.
You can use the DocumentCategorizerME.train() static function to train a collection of documents, where each requires a category tag and the text block to train on. Then, you can initialize the DocumentCategorizerME with the trained model and begin classifying all the rest of your documents.
Once you do this, you can (I think) write the model to a file so you don't have to ever do that again.
This post on extracting keywords and classifying webpages is related and may be helpful. In your example it sounds like you can use tags in lieu of the keyword extraction piece (although you may want to use both in combination). Weka is easy to use, I would definitely recommend giving it a look.
I've recently discovered, to my astonishment (having never really thought about it before), machine-sorting Japanese proper nouns is apparently not possible.
I work on an application that must allow the user to select a hospital from a 3-menu interface. The first menu is Prefecture, the second is City Name, and the third is Hospital. Each menu should be sorted, as you might expect, so the user can find what they want in the menu.
Let me outline what I have found, as preamble to my question:
The expected sort order for Japanese words is based on their pronunciation. Kanji do not have an inherent order (there are tens of thousands of Kanji in use), but the Japanese phonetic syllabaries do have an order: あ、い、う、え、お、か、き、く、け、こ... and on for the fifty traditional distinct sounds (a few of which are obsolete in modern Japanese). This sort order is called 五十音順 (gojuu on jun , or '50-sound order').
Therefore, Kanji words should be sorted in the same order as they would be if they were written in hiragana. (You can represent any kanji word in phonetic hiragana in Japanese.)
The kicker: there is no canonical way to determine the pronunciation of a given word written in kanji. You never know. Some kanji have ten or more different pronunciations, depending on the word. Many common words are in the dictionary, and I could probably hack together a way to look them up from one of the free dictionary databases, but proper nouns (e.g. hospital names) are not in the dictionary.
So, in my application, I have a list of every prefecture, city, and hospital in Japan. In order to sort these lists, which is a requirement, I need a matching list of each of these names in phonetic form (kana).
I can't come up with anything other than paying somebody fluent in Japanese (I'm only so-so) to manually transcribe them. Before I do so though:
Is it possible that I am totally high on fire, and there actually is some way to do this sorting without creating my own mappings of kanji words to phonetic readings, that I have somehow overlooked?
Is there a publicly available mapping of prefecture/city names, from the government or something? That would reduce the manual mapping I'd need to do to only hospital names.
Does anybody have any other advice on how to approach this problem? Any programming language is fine--I'm working with Ruby on Rails but I would be delighted if I could just write a program that would take the kanji input (say 40,000 proper nouns) and then output the phonetic representations as data that I could import into my Rails app.
宜しくお願いします。
For Data, dig Google's Japanese IME (Mozc) data files here.
https://github.com/google/mozc/tree/master/src/data
There is lots of interesting data there, including IPA dictionaries.
Edit:
And you may also try Mecab, it can use IPA dictionary and can convert kanjis to katakana for most of the words
https://taku910.github.io/mecab/
and there is ruby bindings for that too.
https://taku910.github.io/mecab/bindings.html
and here is somebody tested, ruby with mecab with tagger -Oyomi
http://hirai2.blog129.fc2.com/blog-entry-4.html
just a quick followup to explain the eventual actual solution we used. Thanks to all who recommended mecab--this appears to have done the trick.
We have a mostly-Rails backend, but in our circumstance we didn't need to solve this problem on the backend. For user-entered data, e.g. creating new entities with Japanese names, we modified the UI to require the user to enter the phonetic yomigana in addition to the kanji name. Users seem accustomed to this. The problem was the large corpus of data that is built into the app--hospital, company, and place names, mainly.
So, what we did is:
We converted all the source data (a list of 4000 hospitals with name, address, etc) into .csv format (encoded as UTF-8, of course).
Then, for developer use, we wrote a ruby script that:
Uses mecab to translate the contents of that file into Japanese phonetic readings
(the precise command used was mecab -Oyomi -o seed_hospitals.converted.csv seed_hospitals.csv, which outputs a new file with the kanji replaced by the phonetic equivalent, expressed in full-width katakana).
Standardizes all yomikata into hiragana (because users tend to enter hiragana when manually entering yomikata, and hiragana and katakana sort differently). Ruby makes this easy once you find it: NKF.nkf("-h1 -w", katakana_str) # -h1 means to hiragana, -w means output utf8
Using the awesomely conveninent new Ruby 1.9.2 version of CSV, combine the input file with the mecab-translated file, so that the resulting file now has extra columns inserted, a la NAME, NAME_YOMIGANA, ADDRESS, ADDRESS_YOMIGANA, and so on.
Use the data from the resulting .csv file to seed our rails app with its built-in values.
From time to time the client updates the source data, so we will need to do this whenever that happens.
As far as I can tell, this output is good. My Japanese isn't good enough to be 100% sure, but a few of my Japanese coworkers skimmed it and said it looks all right. I put a slightly obfuscated sample of the converted addresses in this gist so that anybody who cared to read this far can see for themselves.
UPDATE: The results are in... it's pretty good, but not perfect. Still, it looks like it correctly phoneticized 95%+ of the quasi-random addresses in my list.
Many thanks to all who helped me!
Nice to hear people are working with Japanese.
I think you're spot on with your assessment of the problem difficulty. I just asked one of the Japanese guys in my lab, and the way to do it seems to be as you describe:
Take a list of Kanji
Infer (guess) the yomigana
Sort yomigana by gojuon.
The hard part is obviously step two. I have two guys in my lab: 高橋 and 高谷. Naturally, when sorting reports etc. by name they appear nowhere near each other.
EDIT
If you're fluent in Japanese, have a look here: http://mecab.sourceforge.net/
It's a pretty popular tool, so you should be able to find English documentation too (the man page for mecab has English info).
I'm not familiar with MeCab, but I think using MeCab is good idea.
Then, I'll introduce another method.
If your app is written in Microsoft VBA, you can call "GetPhonetic" function. It's easy to use.
see : http://msdn.microsoft.com/en-us/library/aa195745(v=office.11).aspx
Sorting prefectures by its pronunciation is not common. Most Japanese are used to prefectures sorted by 「都道府県コード」.
e.g. 01:北海道, 02:青森県, …, 13:東京都, …, 27:大阪府, …, 47:沖縄県
These codes are defined in "JIS X 0401" or "ISO-3166-2 JP".
see (Wikipedia Japanese) :
http://ja.wikipedia.org/wiki/%E5%85%A8%E5%9B%BD%E5%9C%B0%E6%96%B9%E5%85%AC%E5%85%B1%E5%9B%A3%E4%BD%93%E3%82%B3%E3%83%BC%E3%83%89
I have to keep up with structured documents containing things such as requests for proposals, government program reports, threat models and all kinds of things like that. They are in techno-legalese as I would call them: highly structured, with section numbering and 3, 4 and 5 levels of nesting. All in English
I need a more efficient way to locate those paragraphs of nuggets that matter to me. So what I’d like is kind of a local document index/repository, that would allow me to have some standing queries and easily locate sections in documents that talk about my queries. Here’s an example:
I’d like to load in 10 large PDF files, each of say 100 pages. Each PDF contains English text, formatted very nicely into paragraphs and sections.
I’d like to specify that I am interested in “blogging platforms”, “weaknesses in Ruby”, “localization and internationalization”
Ideally then look at a list that showed the section of text, the name of the document, and other information that seemed to be related to and/or include the words and phrases I specified.
I am sure something like this exists. I would call it something like document indexing, document comprehension or structured searching.
Take a look at Lucene: http://lucene.apache.org/ and Solr http://lucene.apache.org/solr/ , which can do most of what you ask. They are not exaclty featherweight though!
There is also this excellent book:
http://www.amazon.com/Building-Search-Applications-Lucene-Lingpipe/dp/0615204252/
Opengrok is another lightweight solution on top of Lucene: http://opengrok.github.io/OpenGrok/
Alternatively, you could have a look at http://www.alfresco.com,
which is not lightweight solution but it is designed exactly for your purposes.