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chretm/gist:fdcefce520ddfa1b66af3c730d4928c0

Created Apr 13, 2017
semantic-similarity-for-short-sentence_python3
 #author : Sujit Pal #Note: this is a python3 updated version of http://sujitpal.blogspot.fr/2014/12/semantic-similarity-for-short-sentences.html # by mathieu Chrétien (mchretien.pro@mail.com) #contributor : Mathieu Chrétien from __future__ import division import nltk from nltk.corpus import wordnet as wn from nltk.corpus import brown import math import numpy as np import sys # Parameters to the algorithm. Currently set to values that was reported # in the paper to produce "best" results. ALPHA = 0.2 BETA = 0.45 ETA = 0.4 PHI = 0.2 DELTA = 0.85 brown_freqs = dict() N = 0 ######################### word similarity ########################## def get_best_synset_pair(word_1, word_2): """ Choose the pair with highest path similarity among all pairs. Mimics pattern-seeking behavior of humans. """ max_sim = -1.0 synsets_1 = wn.synsets(word_1) synsets_2 = wn.synsets(word_2) if len(synsets_1) == 0 or len(synsets_2) == 0: return None, None else: max_sim = -1.0 best_pair = None, None for synset_1 in synsets_1: for synset_2 in synsets_2: sim = wn.path_similarity(synset_1, synset_2) if sim == None: sim = 0 if sim > max_sim: max_sim = sim best_pair = synset_1, synset_2 return best_pair def length_dist(synset_1, synset_2): """ Return a measure of the length of the shortest path in the semantic ontology (Wordnet in our case as well as the paper's) between two synsets. """ l_dist = sys.maxsize if synset_1 is None or synset_2 is None: return 0.0 if synset_1 == synset_2: # if synset_1 and synset_2 are the same synset return 0 l_dist = 0.0 else: wset_1 = set([str(x.name()) for x in synset_1.lemmas()]) wset_2 = set([str(x.name()) for x in synset_2.lemmas()]) if len(wset_1.intersection(wset_2)) > 0: # if synset_1 != synset_2 but there is word overlap, return 1.0 l_dist = 1.0 else: # just compute the shortest path between the two l_dist = synset_1.shortest_path_distance(synset_2) if l_dist is None: l_dist = 0.0 # normalize path length to the range [0,1] return math.exp(-ALPHA * l_dist) def hierarchy_dist(synset_1, synset_2): """ Return a measure of depth in the ontology to model the fact that nodes closer to the root are broader and have less semantic similarity than nodes further away from the root. """ h_dist = sys.maxsize if synset_1 is None or synset_2 is None: return h_dist if synset_1 == synset_2: # return the depth of one of synset_1 or synset_2 h_dist = max([x[1] for x in synset_1.hypernym_distances()]) else: # find the max depth of least common subsumer hypernyms_1 = {x[0]:x[1] for x in synset_1.hypernym_distances()} hypernyms_2 = {x[0]:x[1] for x in synset_2.hypernym_distances()} lcs_candidates = set(hypernyms_1.keys()).intersection( set(hypernyms_2.keys())) if len(lcs_candidates) > 0: lcs_dists = [] for lcs_candidate in lcs_candidates: lcs_d1 = 0 if lcs_candidate in hypernyms_1: lcs_d1 = hypernyms_1[lcs_candidate] lcs_d2 = 0 if lcs_candidate in hypernyms_2: lcs_d2 = hypernyms_2[lcs_candidate] lcs_dists.append(max([lcs_d1, lcs_d2])) h_dist = max(lcs_dists) else: h_dist = 0 return ((math.exp(BETA * h_dist) - math.exp(-BETA * h_dist)) / (math.exp(BETA * h_dist) + math.exp(-BETA * h_dist))) def word_similarity(word_1, word_2): synset_pair = get_best_synset_pair(word_1, word_2) return (length_dist(synset_pair[0], synset_pair[1]) * hierarchy_dist(synset_pair[0], synset_pair[1])) ######################### sentence similarity ########################## def most_similar_word(word, word_set): """ Find the word in the joint word set that is most similar to the word passed in. We use the algorithm above to compute word similarity between the word and each word in the joint word set, and return the most similar word and the actual similarity value. """ max_sim = -1.0 sim_word = "" for ref_word in word_set: sim = word_similarity(word, ref_word) if sim > max_sim: max_sim = sim sim_word = ref_word return sim_word, max_sim def info_content(lookup_word): """ Uses the Brown corpus available in NLTK to calculate a Laplace smoothed frequency distribution of words, then uses this information to compute the information content of the lookup_word. """ global N if N == 0: # poor man's lazy evaluation for sent in brown.sents(): for word in sent: word = word.lower() if word not in brown_freqs: brown_freqs[word] = 0 brown_freqs[word] = brown_freqs[word] + 1 N = N + 1 lookup_word = lookup_word.lower() n = 0 if lookup_word not in brown_freqs else brown_freqs[lookup_word] return 1.0 - (math.log(n + 1) / math.log(N + 1)) def semantic_vector(words, joint_words, info_content_norm): """ Computes the semantic vector of a sentence. The sentence is passed in as a collection of words. The size of the semantic vector is the same as the size of the joint word set. The elements are 1 if a word in the sentence already exists in the joint word set, or the similarity of the word to the most similar word in the joint word set if it doesn't. Both values are further normalized by the word's (and similar word's) information content if info_content_norm is True. """ sent_set = set(words) semvec = np.zeros(len(joint_words)) i = 0 for joint_word in joint_words: if joint_word in sent_set: # if word in union exists in the sentence, s(i) = 1 (unnormalized) semvec[i] = 1.0 if info_content_norm: semvec[i] = semvec[i] * math.pow(info_content(joint_word), 2) else: # find the most similar word in the joint set and set the sim value sim_word, max_sim = most_similar_word(joint_word, sent_set) semvec[i] = PHI if max_sim > PHI else 0.0 if info_content_norm: semvec[i] = semvec[i] * info_content(joint_word) * info_content(sim_word) i = i + 1 return semvec def semantic_similarity(sentence_1, sentence_2, info_content_norm): """ Computes the semantic similarity between two sentences as the cosine similarity between the semantic vectors computed for each sentence. """ words_1 = nltk.word_tokenize(sentence_1) words_2 = nltk.word_tokenize(sentence_2) joint_words = set(words_1).union(set(words_2)) vec_1 = semantic_vector(words_1, joint_words, info_content_norm) vec_2 = semantic_vector(words_2, joint_words, info_content_norm) return np.dot(vec_1, vec_2.T) / (np.linalg.norm(vec_1) * np.linalg.norm(vec_2)) ######################### word order similarity ########################## def word_order_vector(words, joint_words, windex): """ Computes the word order vector for a sentence. The sentence is passed in as a collection of words. The size of the word order vector is the same as the size of the joint word set. The elements of the word order vector are the position mapping (from the windex dictionary) of the word in the joint set if the word exists in the sentence. If the word does not exist in the sentence, then the value of the element is the position of the most similar word in the sentence as long as the similarity is above the threshold ETA. """ wovec = np.zeros(len(joint_words)) i = 0 wordset = set(words) for joint_word in joint_words: if joint_word in wordset: # word in joint_words found in sentence, just populate the index wovec[i] = windex[joint_word] else: # word not in joint_words, find most similar word and populate # word_vector with the thresholded similarity sim_word, max_sim = most_similar_word(joint_word, wordset) if max_sim > ETA: wovec[i] = windex[sim_word] else: wovec[i] = 0 i = i + 1 return wovec def word_order_similarity(sentence_1, sentence_2): """ Computes the word-order similarity between two sentences as the normalized difference of word order between the two sentences. """ words_1 = nltk.word_tokenize(sentence_1) words_2 = nltk.word_tokenize(sentence_2) joint_words = list(set(words_1).union(set(words_2))) windex = {x[1]: x[0] for x in enumerate(joint_words)} r1 = word_order_vector(words_1, joint_words, windex) r2 = word_order_vector(words_2, joint_words, windex) return 1.0 - (np.linalg.norm(r1 - r2) / np.linalg.norm(r1 + r2)) ######################### overall similarity ########################## def similarity(sentence_1, sentence_2, info_content_norm): """ Calculate the semantic similarity between two sentences. The last parameter is True or False depending on whether information content normalization is desired or not. """ return DELTA * semantic_similarity(sentence_1, sentence_2, info_content_norm) + \ (1.0 - DELTA) * word_order_similarity(sentence_1, sentence_2)

ddofer commented May 15, 2017

 Awesome, thanks! (I was having trouble running the original code. I hadn't realized it was due to Python 3 differences :|)