I'm currently having a lot of data (subtitles for films) which contains "compound words" in english.
There is no certain pattern, but in some places words are merged. E.g.:
You got to keep going. They're always looking **forsomething** under the seats.
I don't have specific constraints on language/platform, however there is really lots and lots of data and I won't be able to check results properly. So I need kind of highly-precise solution here.
So what are basically the ways to attack this kind of data?
Related
I have a large set of data (several hundred thousand records) that are unique entries in a CSV. These entries are essentially products that are being listed in a store from a vendor that offers these products. The problem is that while they offer us rights to copy these verbatim or to change wording, I don't want to list them verbatim obviously since Google will slap the ranking for having "duplicate" content. And then, also obviously, manually editing 500,000 items would take a ridiculous amount of time.
The solution, it would seem, would be to leverage fuzzy logic that would take certain phraseology and transform it to something different that would not then be penalized by Google. I have hitherto been unable to find any real library to address this or a solid solution that addresses such a situation.
I am thinking through my own algorithms to perhaps accomplish this, but I hate to reinvent the wheel or, worse, be beaten down by the big G after a failed attempt.
My idea is to simply search for various phrases and words (sans stop words) and then essentially map those to phrases and words that can be randomly inserted that still have equivalent meaning, but enough substance to hopefully not cause a deranking situation.
A solution for Ruby would be optimal, but absolutely not necessary as any language can be used.
Are there any existing algorithms, theories or implementations of a similar scenario that could be used to model or solve such a scenario?
I've got a list of names and I need to split them up into first and last names. Since some names have 2-3 spaces in them, a simple split for a space won't do.
What sort of heuristics do people use to perform the split?
Note that this isn't a duplicate of questions that effectively ask how to split at a space; I'm looking for heuristics and algorithms, not actual code help.
Update: I'm limiting the problem set to English-style names. This is all I need to solve and likely all that anyone approaching this (English language) question will need as well.
I've read a very interesting and comprehensive post on this subject:
http://www.w3.org/International/questions/qa-personal-names
It even suggests to ask yourself whether you really need separate fields for first and last names. It seems to depend on the target region(s) of your application.
Two approaches can help, though not fully solve this problem.
Programatically separate the easy ones, the ones that are not easy get pushed into a different list, "remaining to be split". Manually sort that list. As you manually sort, some heuristics might emerge which could be coded, further reducing the size of the remaining list. If this is a one-time thing, and list is not super massive, this will get the job done.
A closely related problem is when a name is split, but you don't know which is the first and which is last. Some systems work around this problem by doing fuzzy lookups such that if on the first attempt no match is found, flip the first and last name and try again. You didn't say why you need to split the names. If it is to lookup against reference data, consider some kind of similar fuzzy lookup heuristics which allow for trying different splits instead of trying to get the split correct up-front.
Not really an answer, but in this case there really is no perfect answer.
Different countries and regions have different formats for names. For example, Asia the family name is usually first and then given names follows. The West, you’ve got the first name and last name convention, but gets complicated when people double barrel, or include middle names. And then some regions people are only given one name.
Personally, I don’t think there’s one single algorithm that can give you 100% accurate results I’m afraid.
The following assumes English-style surnames. If that's not the case, please update your question.
It's usually safe to assume that the last space character signals the start of a person's surname. But since there are exceptions, one strategy would be to compile a large database of known multi-word surnames from some other source. You could then test for these surnames, and treat them as exceptions.
I want to know the effective algorithms/data structures to identify the below information in streaming data.
Consider a real-time streaming data like twitter. I am mainly interested in the below queries rather than storing the actual data.
I need my queries to run on actual data but not any of the duplicates.
As I am not interested in storing the complete data, it will be difficult for me to identify the duplicate posts. However, I can hash all the posts and check against them. But I would like to identify near duplicate posts also. How can I achieve this.
Identify the top k topics being discussed by the users.
I want to identify the top topics being discussed by users. I don't want the top frequency words as shown by twitter. Instead I want to give some high level topic name of the most frequent words.
I would like my system to be real-time. I mean, my system should be able to handle any amount of traffic.
I can think of map reduce approach but I am not sure how to handle synchronization issues. For example, duplicate posts can reach different nodes and both of them could store them in the index.
In a typical news source, one will be removing any stop words in the data. In my system I would like to update my stop words list by identifying top frequent words across a wide range of topics.
What will be effective algorithm/data structure to achieve this.
I would like to store the topics over a period of time to retrieve interesting patterns in the data. Say, friday evening everyone wants to go to a movie. what will be the efficient way to store this data.
I am thinking of storing it in hadoop distributed file system, but over a period of time, these indexes become so large that I/O will be my major bottleneck.
Consider multi-lingual data from tweets around the world. How can I identify similar topics being discussed across a geographical area?
There are 2 problems here. One is identifying the language being used. It can be identified based on the person tweeting. But this information might affect the privacy of the users. Other idea, could be running it through a training algorithm. What is the best method currently followed for this. Other problem is actually looking up the word in a dictionary and associating it to common intermediate language like say english. How to take care of word sense disambiguation like a same word being used in different contests.
Identify the word boundaries
One possibility is to use some kind of training algorithm. But what is the best approach followed. This is some way similar to word sense disambiguation, because you will be able to identify word boundaries based on the actual sentence.
I am thinking of developing a prototype and evaluating the system rather than the concrete implementation. I think its not possible to scrap the real-time twitter data. I am thinking this approach can be tested on some data freely available online. Any ideas, where I can get this data.
Your feedback is appreciated.
Thanks for your time.
-- Bala
There are a couple different questions buried in here. I can't understand all that you're asking, but here's a the big one as I understand it: You want to categorize messages by topic. You also want to remove duplicates.
Removing duplicates is (relatively) easy. To remove "near" duplicates, you could first remove uninteresting parts from your data. You could start by removing capitalization and punctuation. You could also remove the most common words. Then you could add the resulting message to a Bloom filter. Hashing isn't good enough for Twitter, as the hashed messages wouldn't be much smaller than the full messages. You'd end up with a hash that doesn't fit in memory. That's why you'd use a Bloom filter instead. It might have to be a very large Bloom filter, but it will still be smaller than the hash table.
The other part is a difficult categorization problem. You probably do not want to write this part yourself. There are a number of libraries and programs available for categorization, but it might be hard to find one that fits your needs. An example is the Vowpal Wabbit project, which is a fast online algorithm for categorization. However, it only works on one category at a time. For multiple categories, you would have to run multiple copies and train them separately.
Identifying the language sounds less difficult. Don't try to do something smart like "training", instead put the most common words from each language in a dictionary. For each message, use the language whose words appeared most frequently.
If you want the algorithm to come up with categories on its own, good luck.
I'm not really sure if I'm answering your main question, but you could determine the similarity of two messages by calculating the Levenshtein distance between them. You can think of this as the "edit difference" between two strings (I.E., how many edits would need to be made to one, to convert it to the other).
Hello we have created a very similar demo using api.cortical.io functionality.
There you can create semantic fingerprints of each tweet. (you could also extract the top most keywords or some similar terms, that don't need to actually be part of the tweet).
We have used the fingerprints to filter the twitter stream based on content.
On twistiller.com you can see the result. The public 1% twitter stream is monitored for four different topic areas.
Here's the problem. I have a bunch of large text files with paragraphs and paragraphs of written matter. Each para contains references to a few people (names), and documents a few topics (places, objects).
How do I data mine this pile to assemble some categorised library? ... in general, 2 things.
I don't know what I'm looking for, so I need a program to get the most used words/multiple words ("Jacob Smith" or "bluewater inn" or "arrow").
Then knowing the keywords, I need a program to help me search for related paras, then sort and refine results (manually by hand).
Your question is a tiny bit open-ended :)
Chances are, you will find modules for whatever analysis you want to do in the UIMA framework:
Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user. An example UIM application might ingest plain text and identify entities, such as persons, places, organizations; or relations, such as works-for or located-at.
UIMA is made of many things
UIMA enables applications to be decomposed into components, for example "language identification" => "language specific segmentation" => "sentence boundary detection" => "entity detection (person/place names etc.)". Each component implements interfaces defined by the framework and provides self-describing metadata via XML descriptor files. The framework manages these components and the data flow between them. Components are written in Java or C++; the data that flows between components is designed for efficient mapping between these languages.
You may also find Open Calais a useful API for text analysis; depending on how big your heap of documents is, it may be more or less appropriate.
If you want it quick and dirty -- create an inverted index that stores all locations of words (basically a big map of words to all file ids in which they occur, paragraphs in those files, lines in the paragraphs, etc). Also index tuples so that given a fileid and paragraph you can look up all the neighbors. This will do what you describe, but it takes quite a bit of tweaking to get it to pull up meaningful correlations (some keywords to start you off on your search: information retrieval, TF-IDF, Pearson correlation coefficient).
Looks like you're trying to create an index?
I think Learning Perl has information on finding the frequency of words in a text file, so that's not a particularly hard problem.
But do you really want to know that "the" or "a" is the most common word?
If you're looking for some kind of topical index, the words you actually care about are probably down the list a bit, intermixed with more words you don't care about.
You could start by getting rid of "stop words" at the front of the list to filter your results a bit, but nothing would beat associating keywords that actually reflect the topic of the paragraphs, and that requires context.
Anyway, I could be off base, but there you go. ;)
The problem with what you ask is that you don't know what you're looking for. If you had some sort of weighted list of terms that you cared about, then you'd be in good shape.
Semantically, the problem is twofold:
Generally the most-used words are the least relevant. Even if you use a stop-words file, a lot of chaff remains
Generally, the least-used words are the most relevant. For example, "bluewater inn" is probably infrequent.
Let's suppose that you had something that did what you ask, and produced a clean list of all the keywords that appear in your texts. There would be thousands of such keywords. Finding "bluewater inn" in a list of 1000s of terms is actually harder than finding it in the paragraph (assuming you don't know what you're looking for) because you can skim the texts and you'll find the paragraph that contains "bluewater inn" because of its context, but you can't find it in a list because the list has no context.
Why don't you talk more about your application and process and then perhaps we can help you better??
I think what you want to do is called "entity extraction". This Wikipedia article has a good overview and a list of apps, including open source ones. I used to work on one of the commercial tools in the list, but not in a programming capacity, so I can't help you there.
Ned Batchelder gave a great talk at DevDays Boston about Python.
He presented a spell-corrector written in Python that does pretty much exactly what you want.
You can find the slides and source code here:
http://nedbatchelder.com/text/devdays.html
I recommend that you have a look at R. In particular, look at the tm package. Here are some relevant links:
Paper about the package in the Journal of Statistical Computing: http://www.jstatsoft.org/v25/i05/paper. The paper includes a nice example of an analysis of the R-devel
mailing list (https://stat.ethz.ch/pipermail/r-devel/) newsgroup postings from 2006.
Package homepage: http://cran.r-project.org/web/packages/tm/index.html
Look at the introductory vignette: http://cran.r-project.org/web/packages/tm/vignettes/tm.pdf
More generally, there are a large number of text mining packages on the Natural Language Processing view on CRAN.
Given a few words of input, I want to have a utility that will return a diverse set of relevant terms, phrases, or concepts. A caveat is that it would need to have a large graph of terms to begin with, or else the feature would not be very useful.
For example, submitting "baseball" would return
["shortstop", "Babe Ruth", "foul ball", "steroids", ... ]
Google Sets is the best example I can find of this kind of feature, but I can't use it since they have no public API (and I wont go against their TOS). Also, single-word input doesn't garner a very diverse set of results. I'm looking for a solution that goes off on tangents.
The closest I've experimented with is using WikiPedia's API to search Categories and Backlinks, but there's no way to directly sort those results by "relevance" or "popularity". Without that, the suggestion list is massive and all over the place, which is not immediately useful and very hard to whittle down.
Using A Thesaurus could also work minimally, but that would leave out any proper nouns or tangentially relevant terms (like any of the results listed above).
I would happily reuse an open service, if one exists, but I haven't found anything sufficient.
I'm looking for either a way to implement this either in-house with a decently-populated starting set, or reuse a free service that offers this.
Have a solution? Thanks ahead of time!
UPDATE: Thank you for the incredibly dense & informative answers. I'll choose a winning answer in 6 to 12 months, when I'll hopefully understand what you've all suggested =)
You might be interested in WordNet. It takes a bit of linguistic knowledge to understand the API, but basically the system is a database of meaning-based links between English words, which is more or less what you're searching for. I'm sure I can dig up more information if you want it.
Peter Norvig (director of research at Google) spoke about how they do this at Google (specifically mentioning Google Sets) in a Facebook Tech Talk. The idea is that a relatively simple algorithm on a huge dataset (e.g. the entire web) is much better than a complicated algorithm on a small data set.
You could look at Google's n-gram collection as a starting point. You'd start to see what concepts are grouped together. Norvig hinted that internally Google has up to 7-grams for use in things like Google Translate.
If you're more ambitious, you could download all of Wikipedia's articles in the language you desire and create your own n-gram database.
The problem is even more complicated if you just have a single word; check out this recent thesis for more details on word sense disambiguation.
It's not an easy problem, but it is useful as you mentioned. In the end, I think you'll find that a really successful implementation will have a relatively simple algorithm and a whole lot of data.
Take a look at the following two papers:
Clustering User Queries of a Search Engine [pdf]
Topic Detection by Clustering Keywords [pdf]
Here is my attempt at a very simplified explanation:
If we have a database of past user queries, we can define a similarity function between two queries. For example: number of words in common. Now for each query in our database, we compute its similarity with each other query, and remember the k most similar queries. The non-overlapping words from these can be returned as "related terms".
We can also take this approach with a database of documents containing information users might be searching for. We can define the similarity between two search terms as the number of documents containing both divided by the number of documents containing either. To decide which terms to test, we can scan the documents and throw out words that are either too common ('and', 'the', etc.) or that are too obscure.
If our data permits, then we could see which queries led users to choosing which results, instead of comparing documents by content. For example if we had data that showed us that users searching for "Celtics" and "Lakers" both ended up clicking on espn.com, then we could call these related terms.
If you're starting from scratch with no data about past user queries, then you can try Wikipedia, or the Bag of Words dataset as a database of documents. If you are looking for a database of user search terms and results, and if you are feeling adventurous, then you can take a look at the AOL Search Data.