How can you determine the semantic similarity between two texts in python using WordNet?
The obvious preproccessing would be removing stop words and stemming, but then what?
The only way I can think of would be to calculate the WordNet path distance between each word in the two texts. This is standard for unigrams. But these are large (400 word) texts, that are natural language documents, with words that are not in any particular order or structure (other than those imposed by English grammar). So, which words would you compare between texts? How would you do this in python?
One thing that you can do is:
Kill the stop words
Find as many words as possible that have maximal intersections of synonyms and antonyms with those of other words in the same doc. Let's call these "the important words"
Check to see if the set of the important words of each document is the same. The closer they are together, the more semantically similar your documents.
There is another way. Compute sentence trees out of the sentences in each doc. Then compare the two forests. I did some similar work for a course a long time ago. Here's the code (keep in mind this was a long time ago and it was for class. So the code is extremely hacky, to say the least).
Hope this helps
Related
I try to find, effectively, a similarity between a short phrase and a large corpus. For example, suppose my corpus is the book Moby Dick. This book has tens of thousands of words.
In addition to that, I have a few short phrases. for example:
phrase1 = "Call me Ishmael" # This is the first sentence in the book exactly.
phrase2 = "Call me Isabel" # This is like the previous with changes of few letters from the third word.
phrase3 = "Call me Is mael" #It's a similar sentence but one word split in two.
In addition, I have of course many other sentences that are not similar to sentences from the book.
I wonder what is the generic and effective way to identify sentences that have a sentence similar to them in the book.
What have I tried to do that seems less appropriate to me?
I split all the input sentences into 3/4/5/6 n-grams.
I split all the corpus sentences into 3/4/5/6 n-grams.
Then I tried to find an approximate match (with FuzzySet) between all possible combinations of corpus n-grams and input n-grams (The combinations are required to grasp even cases where words have split or merged.)
It is quite clear to me that this method is very wasteful and probably also not the most accurate for my needs. I would love to understand how best to do it.
You can use corpus-based spell correction followed by fuzzyset. For spell correction, you can use a python implementation of symspell algorithm. Here you can find the list of repository implementing symspell. Use symspell with compounding configuration.
Use a speller trained on the corpus to spell correct short sentences.
Use fuzzyset/fuzzywuzzy to find a similarity score between spell
corrected sentence and each sentence in the corpus.
Define a threshold by experiment, if the similarity is above the
threshold,call it a match
I am working on natural language programming in the German Language in which I need to categorize words according to the meaning of the words. E.g 'Communication', 'Social skills', 'Interpersonal Skills' belongs to 'Communication skills' and so forth.
Basically, the words need to sort based on the similarity of the meaning it has with given set of standard words.
I have tried Levenstein-distance, edit-distance and open-source fuzzy string matching technique but the result are not satisfying.
Best results come from using Longest-common Subsequence the list of words but I want to match the words based on the underlying meaning of the words.
What you are looking for is "semantic similarity". One possible option is to use Spacy or another NLP framework. You would want to explore word 2 vector algorithms to help with you task.
Semantic Similarity with Spacy
I'm trying to analyze a text which is given by lines, and I wish to vectorize the lines using sckit-learn package's TF-IDF-vectorization in python.
The problem is that the vectorization can be done either by words or n-grams but I want them to be done for lines, and I already ruled out a work around that just vectorize each line as a single word (since in that way the words and their meaning wont be considered).
Looking through the documentation I didnt find how to do that, so is there any such option?
You seem to be misunderstanding what the TF-IDF vectorization is doing. For each word (or N-gram), it assigns a weight to the word which is a function of both the frequency of the term (TF) and of its inverse frequency of the other terms in the document (IDF). It makes sense to use it for words (e.g. knowing how often the word "pizza" comes up) or for N-grams (e.g. "Cheese pizza" for a 2-gram)
Now, if you do it on lines, what will happen? Unless you happen to have a corpus in which lines are repeated exactly (e.g. "I need help in Python"), your TF-IDF transformation will be garbage, as each sentence will appear exactly once in the document. And if your sentences are indeed always similar to the punctuation mark, then for all intents and purposes they are not sentences in your corpus, but words. This is why there is no option to do TF-IDF with sentences: it makes zero practical or theoretical sense.
Background: I got a lot of text that has some technical expressions, which are not always standard.
I know how to find the bigrams and filter them.
Now, I want to use them when tokenizing the sentences. So words that should stay together (according to the calculated bigrams) are kept together.
I would like to know if there is a correct way to doing this within NLTK. If not, I can think of various non efficient ways of rejoining all the broken words by checking dictionaries.
The way how topic modelers usually pre-process text with n-grams is they connect them by underscore (say, topic_modeling or white_house). You can do that when identifying big rams themselves. And don't forget to make sure that your tokenizer does not split by underscore (Mallet does if not setting token-regex explicitly).
P.S. NLTK native bigrams collocation finder is super slow - if you want something more efficient look around if you haven't yet or create your own based on, say, Dunning (1993).
I'm trying to write a program to evaluate semantic similarity between texts. I have already compared n-gram frequencies between texts (a lexical measure). I wanted something a bit less shallow than this, and I figured that looking at similarity in sentence construction would be one way to evaluate text similarity.
However, all I can figure out how to do is to count the POS (for example, 4 nouns per text, 2 verbs, etc.). This is then similar to just counting n-grams (and actually works less well than the ngrams).
postags = nltk.pos_tag(tokens)
self.pos_freq_dist = Counter(tag for word,tag in postags)
for pos, freq in self.pos_freq_dist.iteritems():
self.pos_freq_dist_relative[pos] = freq/self.token_count #normalise pos freq by token counts
Lots of people (Pearsons, ETS Research, IBM, academics, etc.) use Parts-of-Speech for deeper measures, but no one says how they have done it. How can Parts-of-Speech be used for a 'deeper' measure of semantic text similarity?
A more sophisticated tagger is required such as http://phpir.com/part-of-speech-tagging/.
You will need to write algorithms and create word banks to determine the meaning or intention of sentences. Semantic analysis is artificial intelligence.
Nouns and capitalized nouns will be the subjects of the content. Adjectives will give some hint as to the polarity of the content. Vagueness, clarity, power, weakness, the types of words used. The possibilities are endless.
Take a look at chapter 6 of the NLTK Book. It should give you plenty of ideas for features you can use to classify text.