I want to take what people chat about in a chat room and do the following information retrieval:
Get the keywords
Ignore all noise words, keep verb an nouns mainly
Perform stemming on the keywords so that I don't store the same keyword in many forms
If a synonym keyword is already stored in my storage then the existing synonym should be used instead of the new keyword
Store the processed keyword in a persistant storage with a reference to the chat message it was located in and the user who uttered it
With this prosessed information I want to slowly get an idea of what people are talking about in chatrooms, and then use this to automatically find related chatrooms etc. based on these keywords.
My question to you is a follows: What is the best C/C++ or .NET tools for doing the above?
I partially agree with #larsmans comment. Your question, in practice, may indeed be more complex than the question you posted.
However, simplifying the question/problem, I guess the answer to your question could be one of Lucene's implementation: Lucene (Java), Lucene.Net (C#) or CLucene (C++).
Following the points in your question:
Lucene would take care of point 1 by using String tokenizers (you can customize or use your own).
For point 2 you could use a TokenFilter like StopFilter so Lucene can read a list of stopwords ("the", "a", "an"...) that it should not use.
For point 3 you could use PorterStemFilter.
Point 4 is a little bit trickier, but could be done using a customized TokenFilter.
Point 1 to 4 are perfomed in the Analysis/tokenization phase, which an Analyzer is responsible.
Regarding point 5, in Lucene you can store Documents with fields. A document can have an arbitrary number and mix of fields. So you could create a single Document for each chat room with all its text concatenated, and have another field of the document reference the chatroom it was extracted from. You will end up with a bunch of Lucene documents that you can compare. So you can compare your current chat room with others to see which one is more similar to the one you are on.
If all you want is a set of the best keywords to describe a chatrom your needs are closer to information extraction/automatic summarization/topic spotting task as #larsmans said. But you can still use Lucene for the parsing/tokenization phase.
*I referenced the Java docs, but CLucene and Lucene.Net have very similar APIs so it won't be much trouble to figure out the differences.
Related
I'm recently interested in NLP, and would like to build up search engine for product recommendation. (Actually I'm always wondering about how search engine for Google/Amazon is built up)
Take Amazon product as example, where I could access all "word" information about one product:
Product_Name Description ReviewText
"XXX brand" "Pain relief" "This is super effective"
By applying nltk and gensim packages I could easily compare similarity of different products and make recommendations.
But here's another question I feel very vague about:
How to build a search engine for such products?
For example, if I feel pain and would like to search for medicine online, I'd like to type-in "pain relief" or "pain", whose searching results should include "XXX brand".
So this sounds more like keyword extraction/tagging question? How should this be done in NLP? I know corpus should contain all but single words, so it's like:
["XXX brand" : ("pain", 1),("relief", 1)]
So if I typed in either "pain" or "relief" I could get "XXX brand"; but what about I searched "pain relief"?
I could come up with idea that directly call python in my javascript for calculate similarities of input words "pain relief" on browser-based server and make recommendation; but that's kind of do-able?
I still prefer to build up very big lists of keywords at backends, stored in datasets/database and directly visualized in web page of search engine.
Thanks!
Even though this does not provide a full how-to answer, there are two things that might be helpful.
First, it's important to note that Google does not only treat singular words but also ngrams.
More or less every NLP problem and therefore also information retrieval from text needs to tackle ngrams. This is because phrases carry way more expressiveness and information than singular tokens.
That's also why so called NGramAnalyzers are popular in search engines, be it Solr or elastic. Since both are based on Lucene, you should take a look here.
Relying on either framework, you can use a synonym analyser that adds for each word the synonyms you provide.
For example, you could add relief = remedy (and vice versa if you wish) to your synonym mapping. Then, both engines would retrieve relevant documents regardless if you search for "pain relief" or "pain remedy". However, you should probably also read this post about the issues you might encounter, especially when aiming for phrase synonyms.
I'm trying to build a local version of the freebase search api using their quad dumps. I'm wondering what algorithm they use to match names? As an example, if you go to freebase.com and type in "Hiking" you get
"Apo Hiking Society"
"Hiking"
"Hiking Georgia"
"Hiking Virginia's national forests"
"Hiking trail"
Wow, a lot of guesses! I hope I don't muddy the waters too much by not guessing too.
The auto-complete box is basically powered by Freebase Suggest which is powered, in turn, by the Freebase Search service. Strings which are indexed by the search service for matching include: 1) the name, 2) all aliases in the given language, 3) link anchor text from the associated Wikipedia articles and 4) identifiers (called keys by Freebase), which includes things like Wikipedia article titles (and redirects).
How the various things are weighted/boosted hasn't been disclosed, but you can get a feel for things by playing with it for while. As you can see from the API, there's also the ability to do filtering/weighting by types and other criteria and this can come into play depending on the context. For example, if you're adding a record label to an album, topics which are typed as record labels will get a boost relative to things which aren't (but you can still get to things of other types to allow for the use case where your target topic doesn't hasn't had the appropriate type applied yet).
So that gives you a little insight into how their service works, but why not build a search service that does what you need since you're starting from scratch anyway?
BTW, pre-Google the Metaweb search implementation was based on top of Lucene, so you could definitely do worse than using that as your starting point. You can read some of the details in the mailing list archive
Probably they use an inverted Index over selected fields, such as the English name, aliases and the Wikipedia snippet displayed. In your application you can achieve that using something like Lucene.
For the algorithm side, I find the following paper a good overview
Zobel and Moffat (2006): "Inverted Files for Text Search Engines".
Most likely it's a trie with lexicographical order.
There are a number of algorithms available: Boyer-Moore, Smith-Waterman-Gotoh, Knuth Morriss-Pratt etc. You might also want to check up on Edit distance algorithms such as Levenshtein. You will need to play around to see which best suits your purpose.
An implementation of such algorithms is the Simmetrics library by the University of Sheffield.
I am developing a program but stuck on a particular hurdle. I need to find words associated with other words. EG "green" might be associated with "environment", "leaf", "earth", "wind", "electric", "hybrid", etc. All I can find is Google Sets. Is there any other resource that is better?
If you have a large text collection (say Wikipedia, Project Gutenberg) you can use co-occurrence scores extract this kind of data. See e.g. Padó and Lapata and the references therein.
I recently built a tool that mines this kind of associations from Wikipedia database dumps by another method. It requires a lot of memory though; other folks have tried to do the same using randomized methods.
If you're still looking for a resource of semantically related words, I've just recently developed an API that takes a query and returns semantically related words. It offers parts of speech, relationships to the query word, and a word similarity measurement.
https://kiingo.co/rapid-associations-api
Disclaimer: I'm the developer of this API.
I'm trying to write a MQL query to format a search result in freebase (the "output" parameter in the search API). I essentially want to find the (simple) values of all the properties of a given search result (without knowing anything about the types of the result a priori). By "simple", I mean only the default properties if the values are complex objects.
E.g., if I search for "Yo La Tengo" and this takes me to the result for "/en/yo_la_tengo", I want to be able to get the group's members (I just need names, not instruments or dates started), albums (again, just names), films contributed to (again, just names), etc.
Is there a simple way to do this with a search output query, given that I know nothing about the types? I imagine there's some sort of reflection magic I can use, and I've tried mucking about with "/type/reflect", but I'm not getting anywhere. I'm brand-new to MQL (though I have extensive SQL experience), so this is a little daunting. Any ideas?
Edit: So to clarify, I think the problem I'm seeing is due to mediator types like "performance" (an actor in a film) or "marriage". E.g., with a query about Yo La Tengo, I can see most (all?) information that I'm interested in, but a similar query about [The Muppet Movie]( freebase.com/api/service/search?limit=1&mql_output=%5B%7B%22%2Ftype%2Freflect%2Fany_reverse%22%3A%5B%7B%7D%5D%2C%22%2Ftype%2Freflect%2Fany_master%22%3A%5B%7B%7D%5D%2C%22%2Ftype%2Freflect%2Fany_value%22%3A%5B%7B%7D%5D%7D%5D&query=The%20Muppet%20Movie -- sorry, SO thinks I'm a spammer so I can't make this a link), I don't see Frank Oz reference at all (probably because his performance is referenced instead). Is there a generic way for me to "follow" mediator types to get all their properties? E.g., is there a single output MQL that would allow me to get the actor in a performance (when linked form a film search result) and give the the spouse in a marriage (when linked from a person)?
Querying not only every property, but then following those properties another ply deep in the graph for all search results is going to be an incredibly expensive operation. What is the use case for this? Do you really have a UI where the user can see and effectively absorb all this information? To answer the question directly though, it's not possible to unpack mediator types automatically using mql_output on the search API.
I'd suggest combining a basic set of information on the search query with a deeper set of information on a topic that the user has expressed interest (e.g. by hovering over). This UI experience would be similar to that of Freebase Suggest.
In the years since the question was originally asked there have been some additional useful things added such as the "notable" pseudo-property which lets you see what the topic is notable for.
Of course everyone also needs to be moving to the new API, so the queries would be:
https://www.googleapis.com/freebase/v1/search?query=%22the%20muppet%20movie%22&limit=1&indent=true
https://www.googleapis.com/freebase/v1/topic/en/the_muppet_movie
AFAIK there is no way to do this in outright MQL, but you can:
Get all the properties of an object or type of object, then
Programmatically construct another MQL query to get those objects you want to know more about.
Look at this example:
[{
"type|=": [
"/film/actor",
"/tv/tv_actor",
"/celebrities/celebrity"
],
"*": [{}]
}]
It grabs all the properties of all objects that have the type actor, tv_actor, or celebrity. When you run it, you'll see all the possible "follow" points you can explore.
This is not exactly what you want, but it should get you closer.
We have a client who is looking for a means to import and categorize a large amount of textual data. This data has to be categorized and it's been suggested that the easiest way to to do this would be to look at the description field and try to match the words held there to see if a category can be derived for that particular record.
It was thought the best way to do this would be matching the words to key words held against each category and if that was unsuccessful then to use some kind of synonym look up to see if this could be used instead. So for example, if a particular record had the word "automobile" in it then a synonym look up could match that word to the word "car" which would be held against the category "vehicle".
Does anyone know of a web service or other means of looking up a dictionary to find synonyms for a particular word? The project manager has suggested buying a Google Enterprise Search license for this but from what I can make out that doesn't offer what these guys are looking for.
Any suggestions of other getting the client what they are looking for would be gratefully accepted.
Thanks! I'll look into Wordnet.
Do you know of any other types of textual classification software products out there. I see there's some discussion of using Bayasian algorithms for this but I can't see any real world examples of it.
The first thing that comes to mind is Wordnet. Wordnet is a human-generated database of words and related words, including synonyms. The Wikipedia Wordnet entry lists several interfaces to Wordnet. I believe some of them are web services.
You can also roll your own. Manning and Schutze's chapter 5 (free PDF) shows ways to do this.
Having said that, are you solving the right problem? How do you build the category list?
Is it a hierarchy? a tag cloud? See Clay Shirky's Ontology is Overrated for a critique of hierarchical categories. I believe that synonyms are less important if you base your classification on sets of words (Naive Bayes, for example) rather than on single words.
You should look at using WordNet. You can visit their website http://wordnet.princeton.edu/ to get more information, but there are libraries available for integrating against them in lots of languages.
Go to their online tool to see the use of it in action here: http://wordnetweb.princeton.edu/perl/webwn. If you look up a word, then click on "S" next to each definition, you'll get a list of semantically related words to that definition.
I also think you should check out software that will allow you to perform "document clustering." Here is an example: http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview. That should help you bootstrap the category creation process.
I think this will help get you a long way toward what you want!
For text classification you can take a look at Apache Mahout.