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.
Related
I'm looking to try and improve my first ever machine learning attempt.
At the moment, I've been getting a good ~90% for my tweet data, using the entire word list as my feature list
I would like to filter out this feature list so it only includes relevant words to gauge sentiment (i.e. words like good,bad,happy, and not robot/car/technology)
Anyone have some advice?
I've made use of their stop words, but then for this, non-sentiment words like "technology" aren't really stopwords
My main approach is to just filter out manually all the words i think wont help, although this assumes I will always use the same input data
One thing that comes to mind is AFINN list, have you run across it? It's a list of English words rated for valence with an integer between minus five (negative) and plus five (positive), created specifically for microblogs. Quick serach for AFINN on Google also spits out a lot of interesting resources.
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!
What are the recommended methods for extracting locations from free text?
What I can think of is to use regex rules like "words ... in location". But are there better approaches than this?
Also I can think of having a lookup hash table table with names for countries and cities and then compare every extracted token from the text to that of the hash table.
Does anybody know of better approaches?
Edit: I'm trying to extract locations from tweets text. So the issue of high number of tweets might also affect my choice for a method.
All rule-based approaches will fail (if your text is really "free"). That includes regex, context-free grammars, any kind of lookup... Believe me, I've been there before :-)
This problem is called Named Entity Recognition. Location is one of the 3 most studied classes (with Person and Organization). Stanford NLP has an open source Java implementation that is extremely powerful: http://nlp.stanford.edu/software/CRF-NER.shtml
You can easily find implementations in other programming languages.
Put all of your valid locations into a sorted list. If you are planning on comparing case-insensitive, make sure the case of your list already is normalized.
Then all you have to do is loop over individual "words" in your input text and at the start of each new word, start a new binary search in your location list. As soon as you find a no-match, you can skip the entire word and proceed with the next.
Possible problem: multi-word locations such as "New York", "3rd Street", "People's Republic of China". Perhaps all it takes, though, is to save the position of the first new word, if you find your bsearch leads you to a (possible!) multi-word result. Then, if the full comparison fails -- possibly several words later -- all you have to do is revert to this 'next' word, in relation to the previous one where you started.
As to what a "word" is: while you are preparing your location list, make a list of all characters that may appear inside locations. Only phrases that contain characters from this list can be considered a valid 'word'.
How fast are the tweets coming in? As in is it the full twitter fire hose or some filtering queries?
A bit more sophisticated approach, that is similar to what you described is using an NLP tool that is integrated to a gazetteer.
Very few NLP tools will keep up to twitter rates, and very few do very well with twitter because of all of the leet speak. The NLP can be tuned for precision or recall depending on your needs, to limit down performing lockups in the gazetteer.
I recommend looking at Rosoka(also Rosoka Cloud through Amazon AWS) and GeoGravy
How would you go about parsing a string of free form text to detect things like locations and names based on a dictionary of location and names? In my particular application there will be tens of thousands if not more entries in my dictionaries so I'm pretty sure just running through them all is out of the question. Also, is there any way to add "fuzzy" matching so that you can also detect substrings that are within x edits of a dictionary word? If I'm not mistaken this falls within the field of natural language processing and more specifically named entity recognition (NER); however, my attempt to find information about the algorithms and processes behind NER have come up empty. I'd prefer to use Python for this as I'm most familiar with that although I'm open to looking at other solutions.
You might try downloading the Stanford Named Entity Recognizer:
http://nlp.stanford.edu/software/CRF-NER.shtml
If you don't want to use someone else's code and you want to do it yourself, I'd suggest taking a look at the algorithm in their associated paper, because the Conditional Random Field model that they use for this has become a fairly common approach to NER.
I'm not sure exactly how to answer the second part of your question on looking for substrings without more details. You could modify the Stanford program, or you could use a part-of-speech tagger to mark proper nouns in the text. That wouldn't distinguish locations from names, but it would make it very simple to find words that are x words away from each proper noun.
[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.