I have two lists: one, the interests of the user; and second, the keywords about a book. I want to recommend the book to the user based on his given interests list. I am using the SequenceMatcher class of Python library difflib to match similar words like "game", "games", "gaming", "gamer", etc. The ratio function gives me a number between [0,1] stating how similar the 2 strings are. But I got stuck at one example where I calculated the similarity between "looping" and "shooting". It comes out to be 0.6667.
for interest in self.interests:
for keyword in keywords:
s = SequenceMatcher(None,interest,keyword)
match_freq = s.ratio()
if match_freq >= self.limit:
#print interest, keyword, match_freq
final_score += 1
break
Is there any other way to perform this kind of matching in Python?
Firstly a word can have many senses and when you try to find similar words you might need some word sense disambiguation http://en.wikipedia.org/wiki/Word-sense_disambiguation.
Given a pair of words, if we take the most similar pair of senses as the gauge of whether two words are similar, we can try this:
from nltk.corpus import wordnet as wn
from itertools import product
wordx, wordy = "cat","dog"
sem1, sem2 = wn.synsets(wordx), wn.synsets(wordy)
maxscore = 0
for i,j in list(product(*[sem1,sem2])):
score = i.wup_similarity(j) # Wu-Palmer Similarity
maxscore = score if maxscore < score else maxscore
There are other similarity functions that you can use. http://nltk.googlecode.com/svn/trunk/doc/howto/wordnet.html. The only problem is when you encounter words not in wordnet. Then i suggest you fallback on difflib.
At first, I thought to regular expressions to perform additional tests to discriminate the matchings with low ratio. It can be a solution to treat specific problem like the one happening with words ending with ing. But that's only a limited case and thre can be numerous other cases that would need to add specific treatment for each one.
Then I thought that we could try to find additional criterium to eliminate not semantically matching words having a letters simlarity ratio enough to be detected as matcging together though the ratio is low,
WHILE in the same time catching real semantically matching terms having low ratio because they are short.
Here's a possibility
from difflib import SequenceMatcher
interests = ('shooting','gaming','looping')
keywords = ('loop','looping','game')
s = SequenceMatcher(None)
limit = 0.50
for interest in interests:
s.set_seq2(interest)
for keyword in keywords:
s.set_seq1(keyword)
b = s.ratio()>=limit and len(s.get_matching_blocks())==2
print '%10s %-10s %f %s' % (interest, keyword,
s.ratio(),
'** MATCH **' if b else '')
print
gives
shooting loop 0.333333
shooting looping 0.666667
shooting game 0.166667
gaming loop 0.000000
gaming looping 0.461538
gaming game 0.600000 ** MATCH **
looping loop 0.727273 ** MATCH **
looping looping 1.000000 ** MATCH **
looping game 0.181818
Note this from the doc:
SequenceMatcher computes and caches detailed information about the
second sequence, so if you want to compare one sequence against many
sequences, use set_seq2() to set the commonly used sequence once and
call set_seq1() repeatedly, once for each of the other sequences.
Thats because SequenceMatcher is based on edit distance or something alike. semantic similarity is more suitable for your case or hybrid of the two.
take a look NLTK pack (code example) as you are using python and maybe this paper
for people using c++ can check this open source project for reference
Related
I'm trying to find out how similar are 2 sentences.
For doing it i'm using gensim word mover distance and since what i'm trying to find it's a similarity i do like it follow:
sim = 1 - wv.wmdistance(sentence_obama, sentence_president)
What i give as an input are 2 strings:
sentence_obama = 'Obama speaks to the media in Illinois'
sentence_president = 'The president greets the press in Chicago'
The model i'm using is the one that you can find on the web: word2vec-google-news-300
I load it with this code:
wv = api.load("word2vec-google-news-300")
It give me reasonable results.
Here it's where the problem starts.
For what i can read from the documentation here it seems the wmd take as input a list of string and not a string like i do!
def preprocess(sentence):
return [w for w in sentence.lower().split() if w not in stop_words]
sentence_obama = preprocess(sentence_obama)
sentence_president = preprocess(sentence_president)
sim = 1 - wv.wmdistance(sentence_obama, sentence_president)
When i follow the documentation i get results really different:
wmd using string as input: 0.5562025871542842
wmd using list of string as input: -0.0174646259300113
I'm really confused. Why is it working with string as input and it works better than when i give what the documentation is asking for?
The function needs a list-of-string-tokens to give proper results: if your results pasing full strings look good to you, it's pure luck and/or poor evaluation.
So: why do you consider 0.556 to be a better value than -0.017?
Since passing the texts as plain strings means they are interpreted as lists-of-single-characters, the value there is going to be a function of how different the letters in the two texts are - and the fact that all English sentences of about the same length have very similar letter-distributions, means most texts will rate as very-similar under that error.
Also, similarity or distance values mainly have meaning in comparison to other pairs of sentences, not two different results from different processes (where one of them is essentially random). You shouldn't consider absolute values that are exceeding some set threshold, or close to 1.0, as definitively good. You should instead consider relative differences, between two similarity/distance values, to mean one pair is more similary/distant than another pair.
Finally: converting a distance (which goes from 0.0 for closest to infinity for furthest) to a similarity (which typically goes from 1.0 for most-similar to -1.0 or 0.0 for least-similar) is not usefully done via the formula you're using, similarity = 1.0 - distance. Because a distance can be larger than 2.0, you could have arbitrarily negative similarities with that approach, and be fooled to think -0.017 (etc) is bad, because it's negative, even if it's quite good across all the possible return values.
Some more typical distance-to-similarity conversions are given in another SO question:
How do I convert between a measure of similarity and a measure of difference (distance)?
Given a list of poker flops and a str as a target:
target = '5c6d2d'
flops = ['5s4d3s', '6s4d2d', '6s5d3s', '6s4s2d']
I am trying to find the closest match to the target. Currently using fuzzywuzzy.process.extract, but sometimes this doesn't return the desired match. And also (more importantly) it doesn't account for ranks properly because the rank of the face cards are represented by letters so 9c9d9s is more similar too 2c2d2h than it is to TcTdTh. Is there a clever way of parsing the target flop to do this with a simple algorithm? Or would it be best to try and train a machine learning model for this?
Side note in case it's relevant: my fuzzywuzzy uses the pure-python SequenceMatcher as I don't have the privileges to install Visual Studio 14 for python-Levenshtein.
EDIT: To clarify, by closest match I mean the closest in terms of flop texture, i.e. the flop that is strategically the most similar (I understand this is somewhat of a difficult qualification). The initial list of examples I gave is actually not too clear, so here is another example with fuzzywuzzy:
>>> target = '8c8h5s'
>>> flops = ['2d2s6c', '7c5s5d', '8s8d7h', '4h3s3d']
>>> matches = process.extract(target, flops)
>>> print(matches)
[('7c5s5d', 50), ('8s8d7h', 50), ('4h3s3d', 33), ('2d2s6c', 17)]
For my purposes '8s8d7h' should score better than '7c5s5d'. So the matching of ranks should be given priority over the matching of suits.
What do you mean by closest match? Do you mean just least number of different characters?
If so, here's how you could do it:
def find_closest(main_flop, flops):
best_match = ''
matching_characters = 0
for flop in flops:
for i, character in enumerate(flop):
if main_flop[i] == character:
matching_characters += 1
if matching characters > max_matching:
best_match = flop
return best_match
On the other hand, if you want to find the "closest" flop in terms of how similar of equities it gives to a full range of hands, then you will have to build something quite complex, or use a library like https://github.com/worldveil/deuces or https://poker.readthedocs.io/en/latest/
I work on text mining problem and need to extract all mentioned of certain keywords. For example, given the list:
list_of_keywords = ['citalopram', 'trazodone', 'aspirin']
I need to find all occurrences of the keywords in a text. That could be easily done with Pandas (assuming my text is read in from a csv file):
import pandas as pd
df_text = pd.read_csv('text.csv')
df_text['matches'] = df_text.str.findall('|'.join(list_of_keywords))
However, there are spelling mistakes in the text and some times my keywords will be written as:
'citalopram' as 'cetalopram'
or
'trazodone' as 'trazadon'
Searching on the web, I found some suggestions how to implement the spell checker, but I am not sure where to insert the spell checker and I reckon that it may slow down the search in the case a very large text.
As another option, it has been suggested to use a wild card with regex and insert in the potential locations of confusion (conceptually written)
.findall('c*t*l*pr*m')
However I am not convinced that I can capture all possible problematic cases. I tried some out-of-the-box spell checkers, but my texts are some-what specific and I need a spell checker that 'knows' my domain (medical domain).
QUESTION
Is there any efficient way to extract keywords from a text including spelling mistakes?
You are right, you cannot capture all possible misspellings with regular expressions.
You do however have options.
You could
Use a trie. A lot of auto complete and spell checking solutions use tries. However most of them operate on a word for word basis. Not on text as a whole
That being said, what you really want is a fuzzy text extractor as you just want to match alternate/slightly wrong spellings and not correct those spellings in the text. So you have more options here as well
Computational genomics has this challenge where they want to search for patterns of base pairs in long sequences. They allow for a certain amount of mismatch in the matched text. So an approximate matching solution like the ones outlined here will help. Those slides have excellent use of the pigeon hole principle to do what you need and the code is open source too!
If you want something a lot less complex, just run an edit distance filter on all the words of the doc and admit only words with an edit distance of k or less.
To expand on what I mean by Edit Distance
(Images/Code borrowed from slides linked above, slides are free to use for anyone i.e no license)
Let us examine a simpler concept of Hamming Distance
def hammingDistance(x, y):
assert len(x) == len(y)
nmm = 0
for i in xrange(0, len(x)):
if x[i] != y[i]:
nmm += 1
return nmm
Hamming distance returns the number of characters that must swapped between 2 equal length strings to make them equal.
But what happens when the strings are not equal length?
Use editDistance Which is the num of characters that must be swapped/inserted/deleted on 2 strings to make them equal
Hamming distance becomes the base case for a recursive algorithm now
def edDistRecursive(x, y):
if len(x) == 0: return len(y)
if len(y) == 0: return len(x)
delt = 1 if x[-1] != y[-1] else 0
diag = edDistRecursive(x[:-1], y[:-1]) + delt
vert = edDistRecursive(x[:-1], y) + 1
horz = edDistRecursive(x, y[:-1]) + 1
return min(diag, vert, horz)
Just call the func above on what you think the word would/should match to (maybe by first looking up a trie). You can even memoize the soln to make it faster as there is a high probability for overlap
I want to find unique strings in a list of strings with a specific percentage (in Python). However, these strings should be significantly different. If there is a small difference between two strings then it's not interesting for me.
I can loop through the strings for finding their similarity percentage but I was wondering if there is a better way to do it?
For example,
String A: He is going to school.
String B: He is going to school tomorrow.
Let's say that these two strings are 80% similar.
Similarity: The string with exact same words in the same order are most similar. A string can be 100% similar with itself
It's a bit vague definition but it works for my use-case.
If you want to check the amount that two sentences are similar and you want to know when they are the exact same word ordering, then you can use single sentence BLEU score.
I would use the sentence_bleu found here: http://www.nltk.org/_modules/nltk/translate/bleu_score.html
You will need to make sure that you do something with your weights for short sentences. An example from something I have done in the past is
from nltk.translate.bleu_score import sentence_bleu
from nltk import word_tokenize
sentence1 = "He is a dog"
sentence2 = "She is a dog"
reference = word_tokenize(sentence1.lower())
hypothesis = word_tokenize(sentence2.lower())
if min(len(hypothesis), len(reference)) < 4:
weighting = 1.0 / min(len(hypothesis), len(reference))
weights = tuple([weighting] * min(len(hypothesis), len(reference)))
else:
weights = (0.25, 0.25, 0.25, 0.25)
bleu_score = sentence_bleu([reference], hypothesis, weights=weights)
Note that single sentence BLEU is quite bad at detecting similar sentences with different word orderings. So if that's what you're interested in then be careful. Other methods you could try are document similarity, Jaccard similarity, and cosine similarity.
I have some data that I get from the Banks using Yodlee and the corresponding transaction messages on the mobile. Both have some description in them - short descriptions.
For example -
string1 = "tatasky_TPSL MUMBA IND"
string2 = "tatasky_TPSL"
They can be matched if one is a completely inside the other. However, some strings like
string1 = "T.G.I Friday's"
string1 = "TGI Friday's MUMBA MAH"
Still need to be matched. Is there a y algorithm which gives a confidence level in matching 2 descriptions ?
You might want to use Normalized edit distance also called levenstien distance levenstien distance wikipedia. So after getting levenstien distance between two strings, you can normalize it by dividing by the length of longest string (or average of those two strings). This normalised socre can act as confidense. You can find some 4-5 python packages of calculating levenstien distance. You can try it online as well edit distance calculator
Alternatively one simple solution is algorithm called longest common subsequence, which can be used here