This is what I want to do:
Given an initial url (eg. http://en.wikipedia.org/wiki/Lists_of_scientists), I want to visit all the links on that page (relevant links of course).
Each link corresponds to another page containing several other links (eg. http://en.wikipedia.org/wiki/List_of_American_scientists). I want to visit each such link so that I can extract xml information from them.
Can this be done using wget? Someone suggested I should use Scrapy, however I am facing problem installing it.
The hierarchy to crawl looks like this: List of Scientists->List of American Scientists->Bryan Hayes (And a lot more scientists).
My target is to extract basic information from these wiki texts, like a person's name, organization, age, etc.
PS: I am a NOOB with good understanding.
Rather than scrape Wikipedia, you can just download the whole thing in one go.
There are tools for scanning categories, so you don't have to crawl the articles yourself.
Of course, you could just skip Wikipedia altogether, as there's already an effort to do this.
If you're still intent on extracting information from Wikipedia itself, start by exploiting Wikipedia's own structure and formatting. Writing a tool to pull information from InfoBoxes would be a good start. If you absolutely want to get information out of the text, the first place to begin is with a named entity recognizer. This finds all of the named entities in text. If you're too lazy to deploy an existing one, you're working on English, and you don't mind a few extra errors, you can just grab sequences of tokens that start with capital letters.
From there, you're probably looking for particular patterns in the data to get information from. You can use a parser, such as the Stanford Parser, to exploit grammatical relations of the language in text. There are also systems that work on finding patterns in strings without any traditional or explicit grammatical knowledge, like Etzioni et al's KnowItAll system. Depending on what exactly you're looking for, one may be better than the other.
Related
My Question
What are the best practices for creating a customized report based on a user form input? Specifically, how do I create an easy to maintain system which takes user input which is collected in a form and generate multiple paragraphs that explains the results of analysis.
Background
I am working on a very large multiyear project with a startup (who is my client). My job is to program analysis and generate reports to users. The pipeline for data looks like this:
Users enter information into a form -> results are calculated based on user input -> reports are displayed to users that share analysis.
It is really important to my client that some of the analysis results are displayed in paragraphs in a non-formal user friendly tone. The challenge is that the form and analysis are quite complex and will only get more complex over time. An example of the type of template for the paragraphs looks something like this:
resultsParagraphText=`Hi ${userName}. We found that the best ice cream flavour for you is ${bestIceCreamFlavor}. These other flavors ${otherFlavors} might be good for you. Here are the reasons why you might enjoy these flavors: ${reasonsWhyGoodFlavors}.
However we would not recommend these other flavors ${badFlavors}. Here are the reasons you should avoid this bad flavors: ${reasonsWhyBadFlavors}.`
These results paragraphs, of which there of many, have several minor problems which combined are significant:
If there is a bug in the code, minor visual errors would be visible to end users (capitalization errors, missing/extra commas, and so on).
A lot of string comparisons (e.g. if answers.previousFlavors.includes("Vanilla")) are required to generate the results paragraphs. Minor errors in the forms (e.g. vanilla in the form is not capitalized so answers.previousFlavors.includes("Vanilla") returns false even when user enters vanilla.) can cause errors in the results paragraph.
Changes in different parts of the project (form, analysis) directly effect how the results paragraph is made. Bad types, differences in string values, null or undefined values not being caught directly have an impact on how the results paragraph is made.
There are many edge cases (e.g. What if the user has no other suitable good flavors for them? The the sentence These other flavors ${otherFlavors} might be good for you. needs to be excluded).
It is hard to write paragraphs that use templates and have a non-formal tone.
and so on.
I have charts and other types of ways to display results and have explained to the client the challenges of sharing the information in paragraph form.
What I am looking for
I need examples, how tos, best practices on how to build a maintainable system for generating customized paragraphs based on user input. I know how to solve each of the individual issues (as they are fairly simple) but in a large project this will become very hard to maintain.
Notes
I have no clue what tags to use for the post. Feel free to edit/add tags if you know more appropriate ones.
The project is planning to use machine learning in the future other parts of the project. If there is a ML/AI solution that is useful please tell me.
I am working primarily in JavaScript, Python, C, and R, but if there is a library or tool in any other language please tell me. Finding a solution is very important to me and I would be willing to learn a lot find a best solution.
To avoid this question being removed because I have rephrased it to avoid asking for personal opinion, instead asking for existing examples or how tos. I can also imagine that others might find a solution fairly useful. If you can edit it to make the question less subjective please do so.
If you have any questions or need clarification feel free to ask. Any help is appreciated.
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.
This is my first time dabbling in NLP so please excuse my ignorance. I'm looking for a method to extract interests/likes/hobbies from users' social profiles. Here is an example where all the interests/likes/hobbies are in bold:
"I consider myself a pretty diverse character... I'm a professional
wrestler, but I'd take a bullet for Wall•E. I train like a one-man genocide machine in the gym, but I cried at
"Armageddon." I'll head bang to AC/DC, and I'm seriously
considering getting a Legend of Zelda tattoo. I'm 420-friendly. I
like to party it up with the frat crowd one night, hang out with
my Burning Man friends the next, play Halo and World of
Warcraft the next, and jam with friends that aren't any younger than
40 the next. My youngest friend is 16, my oldest friend is 66. I'll
sing karaoke at the bars, and I'm my friends' collective
psychiatrist/shoulder."
The profiles are plain text. There are no meta tags or ids associated with any of it, it's just a paragraph of text.
My naiive idea was to take each noun and match it against Freebase to see if it's an activity/artist/movie/book etc. The problem is that although most entities mentioned will be things the user likes, she will also mention things she doesn't like and I have no means of distinguishing the 2.
I have 2 questions:
What sub field of NLP should I be looking at? Some googleable algorithms/techniques/authors would be greatly appreciated.
How hard is this problem?
Thanks!
First, unless using NLP to do this is a particular objective for you, check your problem domain to see if you can avoid it completely.
For instance:
do these profiles have tags (supplied either by the Site or by the
user)?
what does the Site's API make available (assuming that's how you are accessing this data; if you are scraping it, then this doesn't of course apply)? A good example, Facebook. if you read a user's posts, you'll see words like "wrestler", "karaoke", etc. but if you look at what fields are exposed via the Graph API, you'll see that these activities nearly always have an associated FB ID.
I am not a specialist in this field, but I can recommend a couple of resources directed to NLP and which are accessible to the non-specialist or novice. The first is a text processing API. This simple web service uses REST and JSON IO. It is free and seems to have a fairly large rate limit.
This API appears to rely heavily on the excellent Natural Language Tooolkit (NLTK) which is a mature stable library in python, that includes modules directed to the problem in your Question, e.g., Sentiment Analysis, Tagging and Chunk Extraction, etc.
Which particular sub-domain is most relevant to solving the Question in the OP? I don't know, but I suspect there's a module somewhere in the NLTK that does what you need. Finding that module is hopefully just a matter of skimming the API Documentation (which is organized by module); reading the Getting Started section which contains an excellent survey of NLTK's modules as well as demos for all of each of them.
Don't know where to start on this one so hopefully you guys can clear up my question. I have project where email will be searched for specific words/patterns and stored in a structured manner. Something that is done with Trip it.
The article states that they developed a DataMapper
The DataMapper is responsible for taking inbound email messages
addressed to plans [at] tripit.com and transforming them from the
semi-structured format you see in your mail reader into a highly
structured XML document.
There is a comment that also states
If you're looking to build this yourself, reading a little bit about
Wrappers and Wrapper Induction might be helpful
I Googled and read about wrapper induction but it was just too broad of a definition and didn't help me understand how one would go about solving such problem.
Is there some open source project out there that does similar things?
There are a couple of different ways and things you can do to accomplish this.
The first part, which involves getting access to the email content I'll not answer here. Basically, I'll assume that you have access to the text of emails, and if you don't there are some libraries that allow you to connect java to an email box like camel (http://camel.apache.org/mail.html).
So now you've got the email so then what?
A handy thing that could help is that lingpipe (http://alias-i.com/lingpipe/) has an entity recognizer that you can populate with your own terms. Specifically, look at some of their extraction tutorials and their dictionary extractor (http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html) So inside of the lingpipe dictionary extractor (http://alias-i.com/lingpipe/docs/api/com/aliasi/dict/ExactDictionaryChunker.html) you'd simply import the terms you're interested in and use that to associate labels with an email.
You might also find the following question helpful: Dictionary-Based Named Entity Recognition with zero edit distance: LingPipe, Lucene or what?
Really a very broad question, but I can try to give you some general ideas, which might be enough to get started. Basically, it sounds like you're talking about an elaborate parsing problem - scanning through the text and looking to apply meaning to specific chunks. Depending on what exactly you're looking for, you might get some good mileage out of a few regular expressions to start - things like phone numbers, email addresses, and dates have fairly standard structures that should be matchable. Other data points might benefit from some indicator words - the phrase "departing from" might indicate that what follows is an address. The natural language processing community also has a large tool set available for text processing - check out things like parts of speech taggers and semantic analyzers if they're appropriate to what you're trying to do.
Armed with those techniques, you can follow a basic iterative development process: For each data point in your expected output structure, define some simple rules for how to capture it. Then, run the application over a batch of test data and see which samples didn't capture that datum. Look at the samples and revise your rules to catch those samples. Repeat until the extractor reaches an acceptable level of accuracy.
Depending on the specifics of your problem, there may be machine learning techniques that can automate much of that process for you.
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.