Is there an efficient way to convert all variations of words in a corpus (in a language you're not familiar with) to their roots?
In English, for example, this would mean converting plays, played, and playing into play; did, does, done, and doing into do; birds into bird; and so on.
The idea I have is to iterate through the less frequent words and test whether a substring of this word is one of the more frequent words. I don't think this is good because, first, it would not affect irregular verbs and, second, I'm not sure that it's always the "root" of the word that's going to be more frequent than some other variation. This method might also change some words erroneously that are totally different from the frequent word included in them.
The reason I want to do this is that I'm working on a classification problem and figured I'd get better results if I worked better on my preprocessing step. If you've done anything similar or have an idea, please do share.
Thank you!
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 a graduate student focusing on ML and NLP. I have a lot of data (8 million lines) and the text is usually badly written and contains so many spelling mistakes.
So i must go through some text cleaning and vectorizing. To do so, i considered two approaches:
First one:
cleaning text by replacing bad words using hunspell package which is a spell checker and morphological analyzer
+
tokenization
+
convert sentences to vectors using tf-idf
The problem here is that sometimes, Hunspell fails to provide the correct word and changes the misspelled word with another word that don't have the same meaning. Furthermore, hunspell does not reconize acronyms or abbreviation (which are very important in my case) and tends to replace them.
Second approache:
tokenization
+
using some embeddings methode (like word2vec) to convert words into vectors without cleaning text
I need to know if there is some (theoretical or empirical) way to compare this two approaches :)
Please do not hesitate to respond If you have any ideas to share, I'd love to discuss them with you.
Thank you in advance
I post this here just to summarise the comments in a longer form and give you a bit more commentary. No sure it will answer your question. If anything, it should show you why you should reconsider it.
Points about your question
Before I talk about your question, let me point a few things about your approaches. Word embeddings are essentially mathematical representations of meaning based on word distribution. They are the epitome of the phrase "You shall know a word by the company it keeps". In this sense, you will need very regular misspellings in order to get something useful out of a vector space approach. Something that could work out, for example, is US vs. UK spelling or shorthands like w8 vs. full forms like wait.
Another point I want to make clear (or perhaps you should do that) is that you are not looking to build a machine learning model here. You could consider the word embeddings that you could generate, a sort of a machine learning model but it's not. It's just a way of representing words with numbers.
You already have the answer to your question
You yourself have pointed out that using hunspell introduces new mistakes. It will be no doubt also the case with your other approach. If this is just a preprocessing step, I suggest you leave it at that. It is not something you need to prove. If for some reason you do want to dig into the problem, you could evaluate the effects of your methods through an external task as #lenz suggested.
How does external evaluation work?
When a task is too difficult to evaluate directly we use another task which is dependent on its output to draw conclusions about its success. In your case, it seems that you should pick a task that depends on individual words like document classification. Let's say that you have some sort of labels associated with your documents, say topics or types of news. Predicting these labels could be a legitimate way of evaluating the efficiency of your approaches. It is also a chance for you to see if they do more harm than good by comparing to the baseline of "dirty" data. Remember that it's about relative differences and the actual performance of the task is of no importance.
I have an algorithm (that I can't change) that outputs a list of phrases. These phrases are intended to be "topics". However, some of them are meaningless on their own. Take this list:
is the fear
freesat
are more likely to
first sight
an hour of
sue apple
depression and
itunes
How can I filter out those phrases that don't make sense on their own, to leave a list like the following?
freesat
first sight
sue apple
itunes
This will be applied to sets of phrases in many languages, but English is the priority.
It's got to be grammatically acceptable in that it can't rely on other words in the original sentence that it was extracted from; e.g. it can't end in 'and'.
Although this is still an underspecified question, it sounds like you want some kind of grammar checker. I suggest you try applying a part-of-speech tagger to each phrase, compile a list of patterns of POS tags that are acceptable (e.g. anything that ends in a preposition would be unacceptable) and use that to filter your input.
At a high level, it seems that phrases which were only nouns or adjective-noun combos would give much better results.
Examples:
"Blue Shirt"
"Happy People"
"Book"
First of all, this problem can be as complex as you want it to be. For third-party reading/solutions, I came across:
http://en.wikipedia.org/wiki/List_of_natural_language_processing_toolkits
http://research.microsoft.com/en-us/groups/nlp/
http://sharpnlp.codeplex.com/ (note the part of speech tagger)
If you need 100% accuracy, then I wouldn't write such a tool myself.
However, if the problem domain is limited...
I would start by throwing out conjunctions, prepositions, contractions, state-of-being verbs, etc. This is a fairly short list in English (and looks very similar to the stopwords which #HappyTimeGopher suggested).
After that, you could create a dictionary (as an indexed structure, of course) of all acceptable nouns and adjectives and compare each word in the raw phrases to that. Anything which didn't occur in the dictionary and occur in the correct sequence could be thrown out or ranked lower.
This could be useful if you were given 100 input values and wanted to select the best 5. Finding the values in the dictionary would mean that it's likely the word/phrase was good.
I've auto-generated such a dictionary before by building a raw index from thousands of documents pertaining to a vertical industry. I then spent a few hours with SQL and Excel stripping out problems easily spotted by a human. The resulting list wasn't perfect but it eliminated most of the blatantly dumb/pointless terminology.
As you may have guessed, none of this is foolproof, although checking adjective-to-noun sequence would help somewhat. Consider the case of "Greatest Hits" versus "Car Hits [Wall]".
Proper nouns (e.g. person names) don't work well with the dictionary approach, since it's probably not feasible to build a dictionary of all variations of given/surnames.
To summarize:
use a list of stopwords
generate a dictionary of words, classifying them with a part of speech(s)
run raw phrases through dictionary and stopwords
(optional) rank on how confident you are on a match
if needed, accept phrases which didn't violate known patterns (this would handle many proper nouns)
If you've access to the text these phrases were generated from, it may be easier to just create your own topic tags.
Failing that, I'd probably just remove anything that contained a stop word. See this list, for example:
http://www.ranks.nl/resources/stopwords.html
I wouldn't break out POS tagging or anything stronger for this.
It seems you could create a list that filters out three things:
Prepositions: https://en.wikipedia.org/wiki/List_of_English_prepositions
Conjunctions: https://en.wikipedia.org/wiki/Conjunction_(grammar)
Verb forms of to-be: http://www.englishplus.com/grammar/00000040.htm
If you filter on these things you'd get pretty far. Are you more concerned with false negatives or positives? If false negatives aren't a huge problem, this is how I would approach it.
[Caveat] This is not directly a programing question, but it is something that comes up so often in language processing that I'm sure it's of some use to the community.
Does anyone have a good list of uninteresting (English) words that have been tested by more then a casual look? This would include all prepositions, conjunctions, etc... words that may have semantic meaning, but are often frequent in every sentence, regardless of the subject. I've built my own lists from time to time for personal projects but they've been ad-hoc; I continuously add words that I forgotten as they come in.
These words are usually called stop words. The Wikipedia article contains much more information about them, including where to find some lists.
I think you mean stop words.
There's a few links to lists of stop words on Wikipedia, including this one.
As part of teaching myself python I've written a script which allows a user to play hangman. At the moment, the hangman word to be guessed is simply entered manually at the start of the script's code.
I want instead for the script to choose randomly from a large list of english words. This I know how to do - my problem is finding that list of words to work from in the first place.
Does anyone know of a source on the net for, say, 1000 common english words where they can be downloaded as a block of text or something similar that I can work with?
(My initial thought was grabbing a chunk of a novel from project gutenburg [this project is only for my own amusement and won't be available anywhere else so copyright etc doesn't matter hugely to me btw], but anything like that is likely to contain too many names or non-standard words that wouldn't be suitable for hangman. I need text that only has words legal for use in scrabble, basically).
It's a slightly odd question for here I suppose, but actually I thought the answer might be of use not just to me but anyone else working on a project for a wordgame or similar that needs a large seed list of words to work from.
Many thanks for any links or suggestions :)
Would this be useful?
Have you tried /usr/share/dict/words?
Create text list manually
Grab text from Project Gutenberg, Wikipedia or some other source. Go through the text and count how many times each word is found. The words that are found most frequently will be pronouns, conjunctions, etc... Just throw them out.
Proper Nouns will likely be the least frequently found words unless of course your text is a story, then the character names will likely be found quite often. Probably the best way to handle proper nouns is to use many sources and count how many sources the word is found in. Essentially, words that are common among a lot of different sources will likely not be proper nouns. Words that are specific to one text source, you can throw out. This idea is related to tfidf.
Once you have calculated these word frequencies, it's also easy to just look over the words, and tweak your list as necessary.
Use Wordnet
Another idea is to download words from Wordnet. Wordnet tells the parts of speech for a lot of words. You could just stick to nouns and verbs for your purpose.