I'm doing part-of-speech & morphological analysis project for Japanese sentences. Each sentence will have its own webpage. To make this page more visual, I want to show one picture which is somehow related to the sentence. For example, For the sentence "私は学生です" （"I'm a student"), the relevant pictures would be pictures of school, Japanese textbook, students, etc. What I have: part-of-speech tagging for every word. My approach now: use 2-3 nouns from every sentence and retrieve the first image from search results using Bing Images API. Note: all the sentence processing up to this point was done in Java.
Have a couple of questions though:
1) what is better (richer corpus & powerful search), Google Images API, Bing Images API, Flickr API, etc. for searching nouns in Japanese?
2) how do you select the most important noun from the sentence to do the query in Image Search Engine without doing complicated topic modeling, etc.?
Thanks!
Japanese WordNet has links to OpenClipart pictures. That could be another relevant source. They describe it in their paper called "Enhancing the Japanese WordNet".
I thought you would start by choosing any noun before は、が and を and giving these priority - probably in that order.
But that assumes that your part-of-speech tagging is good enough to get は=subject identified properly (as I guess you know that は is not always the subject marker).
I looked at a bunch of sample sentences here with this technique in mind and found it as good as could be expected. Except where none of those are used, which is rarish.
And sentences like this one, where you'd have to consider maybe looking for で and a noun before it in the case where there is no を or は. Because if you notice here, the word 人 (people) really doesn't tell you anything about what's being said. Without parsing context properly, you don't even know if the noun is person or people.
毎年 交通事故で 多くの人が 死にます
(many people die in traffic accidents every year)
But basically, couldn't you implement a priority/fallback type system like this?
BTW I hope your sentences all use kanji, or when you see はし (in one of the sentences linked to) you won't know whether to show a bridge or chopsticks - and showing the wrong one will probably not be good.
Related
I have tags on my website, and I input them one by one when I create a blog post. I love gmail's new feature, that ask you if you want to include X in a mail, if you type Y's name and that you often include both of them in the same messages.
I'd like to do something similar on my website, but I don't know how to represent the tags "related-ness" in an object or database ... thoughts ?
It all boils down to create associations between certain characteristics of your posts and certain tags, and then - when you press the "publish" button - to analyse the new post and propose all tags matched with your post characteristics.
This can be done in several ways from a "totally hard-coded" association to some sort of "learning AI"... and everything in-between.
Hard-coded solutions
This are the simplest algorithms to implement. You should first decide what characteristics of your post are relevant for tagging (e.g.: it's length if you tag them "short" or "long", the presence of photos or videos if you tag them "multimedia-content", etc...). The most obvious is however to focus on which words are used in posts. For example you could build a mapping like this:
tag_hint_words = {'code-development' : ['programming',
'language', 'python', 'function',
'object', 'method'],
'family' : ['Theresa', 'kids',
'uncle Ben', 'holidays']}
Then you would check your post for the presence of the words in the list (the code between [ and ] ) and propose the tag (the word before :) as a possible candidate.
A common approach is to give "scores", or in other word to put a number that indicates the probability a given tag is the right one. For example: if your post would contain the sentence...
After months of programming, we finally left for the summer holidays at uncle Ben's cottage. Theresa and the kids were ecstatic!
...despite the presence of the word "programming" the program should indicate family as the most likely tag to use, as there are many more words hinting.
Learning AI's
One of the obvious limitations of the above method is that - say one day you pick up java beside python - you would probably need to change your code and include words like "java" or "oracle" too. The same applies if you create new tags.
To circumvent this limitation (and have some fun!!) you could try to implement a learning algorithm. Learning algorithms are those who refine their outcome the more you use them (so they indeed... learn!). Some algorithm requires initial training (many spam filters and voice recognition programs need this initial "primer"). Some don't.
I am absolutely no expert on the subject, but two common AI's are: the Naive Bayes Classifier and some flavour of Neural network.
Although the WP pages might look scary, they are surprisingly easy to implement (at least in Python). Here's the recording of a lecture at PyCon 2009 on the subject "Easy AI with Python". I found it very informative and even somehow inspiring! :)
HTH!
You should have a look at this post :
Any suggestions for a db schema for storing related keywords?
If you're looking for a schema for storing related tags it will help.
Relevancy searches where multiple agents play a part are usually done using Collaborative filtering. You might want to give that a look see.
Look up Clustering (Machine Learning algorithm). Don't be intimidated by math, it's a pretty straightforward algorithm. Check out Machine Learning for Hackers for simpler explanations of many Machine Learning algorithms and methods.
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
To make matter more specific:
How to detect people names (seems like simple case of named entity extraction?)
How to detect addresses: my best guess - find postcode (regexes); country and town names and take some text around them.
As for phones, emails - they could be probably caught by various regexes + preprocessing
Don't care about education/working experience at this point
Reasoning:
In order to build a fulltext index on resumes all vulnerable information should be stripped out from them.
P.S. any 3rd party APIs/services won't do as a solution.
The problem you're interested in is information extraction from semi structured sources. http://en.wikipedia.org/wiki/Information_extraction
I think you should download a couple of research papers in this area to get a sense of what can be done and what can't.
I feel it can't be done by a machine.
Every other resume will have a different format and layout.
The best you can do is to design an internal format and manually copy every resume content in there. Or ask candidates to fill out your form (not many will bother).
I think that the problem should be broken up into two search domains:
Finding information relating to proper names
Finding information that is formulaic
Firstly the information relating to proper names could probably be best found by searching for items that are either grammatically important or significant. I.e. English capitalizes only the first word of the sentence and proper nouns. For the gramatical rules you could look for all of the words that have the first letter of the word capitalized and check it against a database that contains the word and the type [i.e. Bob - Name, Elon - Place, England - Place].
Secondly: Information that is formulaic. This is more about the email addresses, phone numbers, and physical addresses. All of these have a specific formats that don't change. Use a regex and use an algorithm to detect the quality of the matches.
Watch out:
The grammatical rules change based on language. German capitalizes EVERY noun. It might be best to detect the language of the document prior to applying your rules. Also, another issue with this [and my resume sometimes] is how it is designed. If the resume was designed with something other than a text editor [designer tools] the text may not line up, or be in a bitmap format.
TL;DR Version: NLP techniques can help you a lot.
I'm curious what the programming terms or methodology is used when Google shows you the "did you mean" link for a word that is made up of multiple words?
For example if I type in "redflower.jpg" It knows to break that up into Red Flower
Is there a common paradigm for doing that sort of operation? Would a Lucene search give you that?
thanks!
If google does not see a lot of matching results for reflowers.jpg, it might then try to cut the words in multiple words until it finds a lot of matching results.
It might also recognize the extension (.jpg), recognize the image extension and then try to find images with the similar name.
If I would have to make an algorithm like this, I would use an huge EXISTING database (either a dictionary or a search engine) and then try what I said in the beginning of my post.
Perhaps they could to look at what other people do when they have searched for redflowers.jpg? Maybe a number of people searched for "redflowers.jpg", didn't click on any links, and then searched for "Red Flower" and found some results worth clicking on.
Of course they would have to take into account that the queries are similar (contain matching strings), otherwise some strange results might appear.
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.