def main(sts_train_file, sts_dev_file):
"""Fits a logistic regression for paraphrase identification, using string similarity metrics as features.
Prints accuracy on held-out data. Data is formatted as in the STS benchmark"""
min_paraphrase = 4.0
max_nonparaphrase = 3.0
# loading train
train_texts_sts, train_y_sts = parse_sts(sts_train_file)
# loading dev
dev_texts_sts, dev_y_sts = parse_sts(sts_dev_file)
def main(sts_train_file, sts_dev_file, w2v_file):
"""Fits a logistic regression for paraphrase identification, using string similarity metrics and vector similarity
as features. Prints results on held-out data. Data is formatted as in the STS benchmark"""
min_paraphrase = 4.0
max_nonparaphrase = 3.0
# TODO 1: Load data partitions and convert to paraphrase dataset as in the lab
# You will train a logistic regression on the TRAIN partition
train_texts_sts, train_y_sts = parse_sts(sts_train_file)
# You will evaluate predictions on the VALIDATION partition
dev_texts_sts, dev_y_sts = parse_sts(sts_dev_file)
def main(sts_data):
"""Calculate NIST metric for pairs of strings
Data is formatted as in the STS benchmark"""
# TODO 1: define a function to read the data in util
texts, labels = parse_sts(sts_data)
print(f"Found {len(texts)} STS pairs")
# take a sample of sentences so the code runs fast for faster debugging
# when you're done debugging, you may want to run this on more!
sample_text = texts[120:140]
sample_labels = labels[120:140]
# zip them together to make tuples of text associated with labels
sample_data = zip(sample_labels, sample_text)
scores = []
for label, text in enumerate(sample_data):
t1, t2 = text
print(f"Sentences: {t1}\t{t2}")
# TODO 2: Calculate NIST for each pair of sentences
# calculate NIST(a,b) and NIST(b,a) and
# catch any exceptions and assign 0.0 for that part of the score
nist_score = 0.0
print(f"Label: {label}, NIST: {nist_score:0.02f}\n")
scores.append(score)
def main(sts_data):
texts, labels = parse_sts(sts_data)
# TODO 1: get a single list of texts to determine vocabulary and document frequency
# create a TfidfVectorizer
# fit to the training data
# TODO 2: Can normalization like removing stopwords remove differences that aren't meaningful?
# fill in preprocess_text above
preproc_train_texts = [preprocess_text(text) for text in texts]
# TODO 3: Learn another TfidfVectorizer for preprocessed data
# Use token_pattern "\S+" in the TfidfVectorizer to split on spaces
# TODO 4: compute cosine similarity for each pair of sentences, both with and without preprocessing
cos_sims = []
cos_sims_preproc = []
for pair in texts:
t1, t2 = pair
# TODO 5: measure the correlations
pearson = 0.0
preproc_pearson = 0.0
print(f"default settings: r={pearson:.03}")
print(f"preprocessed text: r={preproc_pearson:.03}")
def main(sts_dev, w2v_file):
# load the texts
dev_texts, dev_y = parse_sts(sts_dev)
# load word2vec using gensim KeyedVectors object
w2v_vecs = None
# get cosine similarities of every pair in dev
# if either sentence is completely out of vocabulary, record "0" as the similarity
cos_sims_mean = []
cos_sims_product = []
pearson_mean = 0
print(f"word2vec mean pearsons: r={pearson_mean[0]:.03}")
pearson_prod = 0
print(f"word2vec product pearsons: r={pearson_prod[0]:.03}")
def main(sts_data):
"""Calculate NIST metric for pairs of strings
Data is formatted as in the STS benchmark"""
# read the dataset
texts, labels = parse_sts(sts_data)
print(f"Found {len(texts)} STS pairs")
for i,pair in enumerate(texts[120:140]):
label = labels[i+120]
t1, t2 = pair
print(f"Sentences: {t1}\t{t2}")
# TODO: Calculate for each pair of sentences
# catch any exceptions and assign 0.0
nist_score = 0.0
print(f"Label: {label}, NIST: {nist_score:0.02f}\n")
def main(sts_data):
"""Transform a semantic textual similarity dataset into a paraphrase identification.
Data is formatted as in the STS benchmark"""
max_nonparaphrase = 3.0
min_paraphrase = 4.0
# read the dataset
texts, labels = parse_sts(sts_data)
labels = np.asarray(labels)
pi_texts, pi_labels = sts_to_pi(texts, labels)
# calculate to check your split agrees with mine
num_nonparaphrase = 0
num_paraphrase = 0
# 957 for dev
print(f"{num_nonparaphrase} non-paraphrase")
# 264 for dev
print(f"{num_paraphrase} paraphrase")
# Instantiate a TFIDFVectorizer to create representations for sentences
# compute cosine similarity for each pair of sentences
# use a threshold of 0.7 to convert each similarity score into a paraphrase prediction
cos_sims_preproc = []
predictions = np.asarray(cos_sims_preproc) > 0.7
# calculate and print precision and recall statistics for your system
num_pred = 0
print(f"Number predicted paraphrase: {num_pred}")
num_pos = 0
print(f"Number positive: {num_pos}")
num_true_pos = 0
print(f"Number true positive: {num_true_pos}")
precision = 0
recall = 0
print(f"Scores: precision {precision:0.03}\trecall {recall:0.03}")
def main(sts_data):
"""Calculate pearson correlation between semantic similarity scores and string similarity metrics.
Data is formatted as in the STS benchmark"""
# TODO 1: read the dataset; implement in util.py
texts, labels = parse_sts(sts_data)
print(f"Found {len(texts)} STS pairs")
# TODO 2: Calculate the metrics here
score_types = [
"NIST", "BLEU", "Word Error Rate", "Longest common substring",
"Edit Distance"
]
# Sample code to print results. You can alter the printing as you see fit. It is most important to put the results
# in a table in the README
print(f"Semantic textual similarity for {sts_data}\n")
for metric_name in score_types:
score = 0.0
print(f"{metric_name} correlation: {score:.03f}")
def main(sts_data):
"""Calculate pearson correlation between semantic similarity scores and string similarity metrics.
Data is formatted as in the STS benchmark"""
# read the dataset
# TODO: implement in util.py
texts, labels = parse_sts(sts_data)
print(f"Found {len(texts)} STS pairs")
score_types = ["NIST", "BLEU", "Word Error Rate", "Longest common substring", "Levenshtein distance"]
scores = {score_type: [] for score_type in score_types}
# TODO: Calculate the metrics here to fill the lists in scores
# This can stay as-is to print similar output to the sample
print(f"Semantic textual similarity for {sts_data}\n")
for metric_name, dists in scores.items():
score, sig = pearsonr(dists, labels)
print(f"{metric_name} correlation: {score:.03}")
def main(sts_dev, w2v_file):
# TODO 1: load the texts
dev_texts, dev_y = parse_sts(sts_dev)
# TODO 2: load word2vec using gensim KeyedVectors object
# WARNING: you may need to downgrade gensim to version 3.4
w2v_vecs = None
# TODO 3: Define the functions above that compose word representations into sentence representations
# TODO 4: get cosine similarities of every sentence pair in dev
# if either sentence is completely out of vocabulary, record "0" as the similarity for the pair
cos_sims_mean = []
cos_sims_product = []
# TODO 5: Measure correlation with STS labels for the two ways of computing word2vec sentence representations
pearson_mean = 0
print(f"word2vec mean pearsons: r={pearson_mean[0]:.03}")
pearson_prod = 0
print(f"word2vec product pearsons: r={pearson_prod[0]:.03}")
