I am trying to find readymade service for text comparison (the meaning of sentences including consideration of synonyms)
I found out that AWS Comprehend does not support text comparison. I am looking for a trusted way to quickly implement.
The information "text comparison" is very broad. If there is an example of the expected functionality, we can help better. Following are some points regarding semantics with Python's library for WORDNET.
This offers a way to identify synonyms, hypernymy, and hyponymy.
This also offers a way to measure the distance between two words and thereby helps in comparing the "proximity" of those words.
Please note that WORDNET is a collection of NOUNS and does not have all the words in English.
I've used SprinkeML to do word comparisons and it's worked well.
Related
I have a list of let's say "forbidden sentences" (1000 of them, each with around 40 words). I want to create a tool that will find and mark them in a given document.
The problem is that in such document this forbidden sentence can be expressed differently than it is on this list keeping the same meaning but changed by using synonyms, a few words more or less, different word order, punctuation, grammar etc. The fact that this is all in Polish is not making things easier with each noun, pronoun, and adjective having 14 cases in total plus modifiers and gender that changes the words further. I was also thinking about making it so that the found sentences are ranked by the probability of them being forbidden with some displaying less resemblance.
I studied IT for two years but I don't have much knowledge in NLP. Do you think this is possible to be done by an amateur? Could you give me some advice on where to start, what tools to use best to put it all together? No need to be fancy, just practical. I was hoping to find some ready to use code cause i imagine this is sth that was made before. Any ideas where to find such resources or what keywords to use while searching? I'd really appreciate some help cause I'm very new to this and need to start with the basics.
Thanks in advance,
Kamila
Probably the easiest first try will be to use polish SpaCy, which is an extension of popular production-ready NLP library to support polish language.
http://spacypl.sigmoidal.io/#home
You can try to do it like this:
Split document into sentences.
Clean these sentences with spacy (deleting stopwords, punctuation, doing lemmatization - it will help you with many differnet versions of the same word)
Clean "forbidden sentences" as well
Prepare vector representation of each sentence - you can use spaCy methods
Calculate similarity between sentences - cosine similarity
You can set threshold, from which if sentences of document is similar to any of "forbidden sentences" it will be treated as forbidden
If anything is not clear let me know.
Good luck!
I am looking into extracting the meaning of expressions used in everyday speaking. For an instance, it is apparent to a human that the sentence The meal we had at restaurant A tasted like food at my granny's. means that the food was tasty.
How can I extract this meaning using a tool or a technique?
The method I've found so far is to first extract phrases using Stanford CoreNLP POS tagging, and use a Word Sense Induction tool to derive the meaning of the phrase. However, as WSI tools are used to get the meaning of words when they have multiple meanings, I am not sure if it would be the best tool to use.
What would be the best method to extract the meanings? Or is there any tool that can both identify phrases and extract their meanings?
Any help is much appreciated. Thanks in advance.
The problem you pose is a difficult one. You should use tools from Sentiment Analysis to get a gist of the sentence emotional message. There are more sophisticated approaches which attempt at extracting what quality is assigned to what object in the sentence (this you can get from POS-tagged sentences + some hand-crafted Information Extraction rules).
However, you may want to also explore paraphrasing the more formal language to the common one and look for those phrases. For that you would need to a good (exhaustive) dictionary of common expressions to start with (there are sometimes slang dictionaries available - but I am not aware of any for English right now). You could then map the colloquial ones to some more formal ones which are likely to be caught by some embedding space (frequently used in Sentiment Analysis).
I have some texts in different languages and, potentially, with some typo or other mistake, and I want to retrieve their own vocabulary. I'm not experienced with NLP in general, so maybe I use some word improperly.
With vocabulary I mean a collection of words of a single language in which every word is unique and the inflections for gender, number, or tense are not considered (e.g. think, thinks and thought are are all consider think).
This is the master problem, so let's reduce it to the vocabulary retrieving of one language, English for example, and without mistakes.
I think there are (at least) three different approaches and maybe the solution consists of a combination of them:
search in a database of words stored in relation with each others. So, I could search for thought (considering the verb) and read the associated information that thought is an inflection of think
compute the "base form" (a word without inflections) of a word by processing the inflected form. Maybe it can be done with stemming?
use a service by any API. Yes, I accept also this approach, but I'd prefer to do it locally
For a first approximation, it's not necessary that the algorithm distinguishes between nouns and verbs. For instance, if in the text there were the word thought like both noun and verb, it could be considered already present in the vocabulary at the second match.
We have reduced the problem to retrieve a vocabulary of an English text without mistakes, and without consider the tag of the words.
Any ideas about how to do that? Or just some tips?
Of course, if you have suggestions about this problem also with the others constraints (mistakes and multi-language, not only Indo-European languages), they would be much appreciated.
You need lemmatization - it's similar to your 2nd item, but not exactly (difference).
Try nltk lemmatizer for Python or Standford NLP/Clear NLP for Java. Actually nltk uses WordNet, so it is really combination of 1st and 2nd approaches.
In order to cope with mistakes use spelling correction before lemmatization. Take a look at related questions or Google for appropriate libs.
About part of speech tag - unfortunately, nltk doesn't consider POS tag (and context in general), so you should provide it with the tag that can be found by nltk pos tagging. Again, it is already discussed here (and related/linked questions). I'm not sure about Stanford NLP here - I guess it should consider context, but I was sure that NLTK does so. As I can see from this code snippet, Stanford doesn't use POS tags, while Clear NLP does.
About other languages - google for lemmatization models, since algorithm for most languages (at least from the same family) is almost the same, differences are in training data. Take a look here for example of German; it is a wrapper for several lemmatizers, as I can see.
However, you always can use stemmer at cost of precision, and stemmer is more easily available for different languages.
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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.
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How does the Google “Did you mean?” Algorithm work?
Suppose you have a search system already in your website. How can you implement the "Did you mean:<spell_checked_word>" like Google does in some search queries?
Actually what Google does is very much non-trivial and also at first counter-intuitive. They don't do anything like check against a dictionary, but rather they make use of statistics to identify "similar" queries that returned more results than your query, the exact algorithm is of course not known.
There are different sub-problems to solve here, as a fundamental basis for all Natural Language Processing statistics related there is one must have book: Foundation of Statistical Natural Language Processing.
Concretely to solve the problem of word/query similarity I have had good results with using Edit Distance, a mathematical measure of string similarity that works surprisingly well. I used to use Levenshtein but the others may be worth looking into.
Soundex - in my experience - is crap.
Actually efficiently storing and searching a large dictionary of misspelled words and having sub second retrieval is again non-trivial, your best bet is to make use of existing full text indexing and retrieval engines (i.e. not your database's one), of which Lucene is currently one of the best and coincidentally ported to many many platforms.
Google's Dr Norvig has outlined how it works; he even gives a 20ish line Python implementation:
http://googlesystem.blogspot.com/2007/04/simplified-version-of-googles-spell.html
http://www.norvig.com/spell-correct.html
Dr Norvig also discusses the "did you mean" in this excellent talk. Dr Norvig is head of research at Google - when asked how "did you mean" is implemented, his answer is authoritive.
So its spell-checking, presumably with a dynamic dictionary build from other searches or even actual internet phrases and such. But that's still spell checking.
SOUNDEX and other guesses don't get a look in, people!
Check this article on wikipedia about the Levenshtein distance. Make sure you take a good look at Possible improvements.
I was pleasantly surprised that someone has asked how to create a state-of-the-art spelling suggestion system for search engines. I have been working on this subject for more than a year for a search engine company and I can point to information on the public domain on the subject.
As was mentioned in a previous post, Google (and Microsoft and Yahoo!) do not use any predefined dictionary nor do they employ hordes of linguists that ponder over the possible misspellings of queries. That would be impossible due to the scale of the problem but also because it is not clear that people could actually correctly identify when and if a query is misspelled.
Instead there is a simple and rather effective principle that is also valid for all European languages. Get all the unique queries on your search logs, calculate the edit distance between all pairs of queries, assuming that the reference query is the one that has the highest count.
This simple algorithm will work great for many types of queries. If you want to take it to the next level then I suggest you read the paper by Microsoft Research on that subject. You can find it here
The paper has a great introduction but after that you will need to be knowledgeable with concepts such as the Hidden Markov Model.
I would suggest looking at SOUNDEX to find similar words in your database.
You can also access google own dictionary by using the Google API spelling suggestion request.
You may want to look at Peter Norvig's "How to Write a Spelling Corrector" article.
I believe Google logs all queries and identifies when someone makes a spelling correction. This correction may then be suggested when others supply the same first query. This will work for any language, in fact any string of any characters.
http://en.wikipedia.org/wiki/N-gram#Google_use_of_N-gram
I think this depends on how big your website it. On our local Intranet which is used by about 500 member of staff, I simply look at the search phrases that returned zero results and enter that search phrase with the new suggested search phrase into a SQL table.
I them call on that table if no search results has been returned, however, this only works if the site is relatively small and I only do it for search phrases which are the most common.
You might also want to look at my answer to a similar question:
"Similar Posts" like functionality using MS SQL Server?
If you have industry specific translations, you will likely need a thesaurus. For example, I worked in the jewelry industry and there were abbreviate in our descriptions such as kt - karat, rd - round, cwt - carat weight... Endeca (the search engine at that job) has a thesaurus that will translate from common misspellings, but it does require manual intervention.
I do it with Lucene's Spell Checker.
Soundex is good for phonetic matches, but works best with peoples' names (it was originally developed for census data)
Also check out Full-Text-Indexing, the syntax is different from Google logic, but it's very quick and can deal with similar language elements.
Soundex and "Porter stemming" (soundex is trivial, not sure about porter stemming).
There's something called aspell that might help:
http://blog.evanweaver.com/files/doc/fauna/raspell/classes/Aspell.html
There's a ruby gem for it, but I don't know how to talk to it from python
http://blog.evanweaver.com/files/doc/fauna/raspell/files/README.html
Here's a quote from the ruby implementation
Usage
Aspell lets you check words and suggest corrections. For example:
string = "my haert wil go on"
string.gsub(/[\w\']+/) do |word|
if !speller.check(word)
# word is wrong
puts "Possible correction for #{word}:"
puts speller.suggest(word).first
end
end
This outputs:
Possible correction for haert:
heart
Possible correction for wil:
Will
Implementing spelling correction for search engines in an effective way is not trivial (you can't just compute the edit/levenshtein distance to every possible word). A solution based on k-gram indexes is described in Introduction to Information Retrieval (full text available online).
U could use ngram for the comparisment: http://en.wikipedia.org/wiki/N-gram
Using python ngram module: http://packages.python.org/ngram/index.html
import ngram
G2 = ngram.NGram([ "iis7 configure ftp 7.5",
"ubunto configre 8.5",
"mac configure ftp"])
print "String", "\t", "Similarity"
for i in G2.search("iis7 configurftp 7.5", threshold=0.1):
print i[1], "\t", i[0]
U get:
>>>
String Similarity
0.76 "iis7 configure ftp 7.5"
0.24 "mac configure ftp"
0.19 "ubunto configre 8.5"
Why not use google's did you mean in your code.For how see here
http://narenonit.blogspot.com/2012/08/trick-for-using-googles-did-you-mean.html