def run_loop(context, card, card_tag): list_of_sentences = [] list_of_paragraphs = [] if granularity_level == "Sent": for paragraph in segmenter.analyze(card): for sentence in paragraph: ## sentence level summarization set_str = "" for token in sentence: set_str += token.spacing set_str += token.value list_of_sentences.append(set_str) word_list = embed(card_tag, list_of_sentences, 0, 0) elif granularity_level == "Paragraph": for paragraph in segmenter.analyze(card): set_str = "" for sentence in paragraph: ## sentence level summarization #set_str = "" for token in sentence: set_str += token.spacing set_str += token.value list_of_paragraphs.append(set_str) word_list = embed(card_tag, list_of_paragraphs, 0, 0) elif granularity_level == "Word": card_as_sentence = Sentence(card) card_words, card_words_org = create_ngram(context, card, card_tag) word_list = embed(card_tag, card_as_sentence, card_words, card_words_org) #print(word_list) return word_list
def tokenize_text(self, text): paragraphs = [] for paragraph in segmenter.analyze(text): sentences = [] for sentence in paragraph: sentences.append(' '.join([token.value for token in sentence])) paragraphs.append('\n'.join(sentences)) return '\n\n'.join(paragraphs)
def sentenceify(text): return [ sl for l in [[ ''.join([t.spacing + t.value for t in s]) for s in p if len(s) < MAX_SENT_LEN ] for p in segmenter.analyze(text)] for sl in l if any(map(lambda x: x.isalpha(), sl)) ]
def test_analyze(self): offset = 0 for paragraph in segmenter.analyze(DOCUMENT): for sentence in paragraph: for token in sentence: if token.value: offset = DOCUMENT.index(token.value, offset) self.assertEqual(offset, token.offset, repr(token)) offset += len(token.value)
def readability_stats(dataframe, row, i, current_column, new_column, readability_group, readability_measure): this_comment = row[current_column] tokenized = '\n\n'.join('\n'.join(' '.join(token.value for token in sentence) for sentence in paragraph) for paragraph in segmenter.analyze(this_comment)) this_result = readability.getmeasures(tokenized, lang='en') c.df[dataframe].at[ i, new_column] = this_result[readability_group][readability_measure]
def syntok_ssplit(text: str, ignore_newlines=True) -> Tuple[str, int, int]: if ignore_newlines: # remove only single newlines, assume multiples are paragraph breaks text = ' '.join(re.split(r'(?<!\n)\n(?!\n)', text)) start = 0 for paragraph in syntok_segmenter.analyze(text): for sentence in paragraph: sentence = ' '.join(tok.value for tok in sentence) end = start + len(sentence) yield sentence, start, end start = end
def run_readability(texts): out = [] for text in texts: tokenized = '\n\n'.join( '\n'.join( ' '.join(token.value for token in sentence) for sentence in paragraph) for paragraph in segmenter.analyze(text)) results = readability.getmeasures(tokenized, lang='en') data = {} for key in results: data[key.replace(' ', '')] = dict(results[key]) out.append(data) return out
def sentence_tokenize( self, text: str, ) -> List[str]: """Split a text into sentences using syntok package Args: text: text to be split """ lst_sentences = [] for paragraph in segmenter.analyze(text): for sentence in paragraph: sentence = "".join(map(str, sentence)).lstrip() sentence = self.postprocess(sentence) lst_sentences.append(sentence) return lst_sentences
def run_loop(context, card, card_tag): list_of_sentences = [] if sent_level: for paragraph in segmenter.analyze(card): for sentence in paragraph: ## sentence level summarization set_str = "" for token in sentence: set_str += token.spacing set_str += token.value list_of_sentences.append(set_str) word_list = embed(card_tag, list_of_sentences, 0, 0) else: card_as_sentence = Sentence(card) card_words, card_words_org = create_ngram(context, card, card_tag) word_list = embed(card_tag, card_as_sentence, card_words, card_words_org) return word_list
def add_doc(writer, path, processed_doc_path): fileobj = open(path, "r") content = fileobj.read() fileobj.close() # tokenize tokenized_str = '' for sent in [sent for para in segmenter.analyze(content) for sent in para]: tokens = tokenizer.convert_ids_to_tokens( tokenizer([t.value for t in sent], is_pretokenized=True, add_special_tokens=False)['input_ids']) tokenized_str += ' '.join(tokens) + '\n' filename = os.path.basename(path) out = open(processed_doc_path, 'w') print(tokenized_str, file=out) out.close() writer.add_document(filename=filename, content=tokenized_str)
def sentences(text: str) -> StringGenerator: """ Get the sentences of a document. Parameters ---------- text : str The text to tokenize. Returns ------- Generator of str The sentences, one after the other. """ for paragraph in segmenter.analyze(text): for sentence in paragraph: orig_sentence = "" for t in sentence: orig_sentence += t.spacing + t.value yield orig_sentence
tree_left = strip_ns_prefix(etree.parse(sys.argv[1], parser)) tree_right = strip_ns_prefix(etree.parse(sys.argv[2], parser)) (lstring, lsegments) = readtree(tree_left) (rstring, rsegments) = readtree(tree_right) for i in range(len(lstring)): if lstring[i] != rstring[i]: print("abort: normalized text missmatches on char index " + i, file=sys.stderr) sys.exit(1) string = lstring sentences = [{ 'start': sentence[0]._offset, 'end': sentence[-1]._offset + len(sentence[-1].value) } for paragraph in segmenter.analyze(string) for sentence in paragraph] # TODO implement alignments alignment_scores = [{ 'name': 'random', 'scores': [random.random() for s in sentences] }, { 'name': 'random2', 'scores': [random.random() for s in sentences] }] output = { 'sentences': sentences, 'alignment_scores': alignment_scores, 'text': string, 'left_segments': {seg['id']: seg
for i in sentence: sen += i sentence = sen sentence = sentence.split('?') sen = '' for i in sentence: sen += i sentence = sen sentence = sentence.split(',') sen = '' for i in sentence: sen += i sentence = sen sentence += ' ' expected.append(sentence) print(expected) print() document = '' random.shuffle(expected) for phrase in expected: document += phrase for paragraph in segmenter.analyze(document): for sentence in paragraph: for token in sentence: # exactly reproduce the input # and do not remove "imperfections" print(token.spacing, token.value, sep='', end='') print() print("\n") # reinsert paragraph separators
def predict(): # Works only for a single sample if request.method == 'POST': if not request.form.getlist('review'): return render_template( 'home.html', predicted='You need to enter a review for the restaurant') if not request.form.getlist('star'): return render_template( 'home.html', predicted= 'You need to give a number of stars for the restaurant') text = request.form.getlist('review')[0] stars = request.form.getlist('star')[0] tokenized = '\n\n'.join('\n'.join(' '.join(token.value for token in sentence) for sentence in paragraph) for paragraph in segmenter.analyze(text)) nested_feature_dict = readability.getmeasures(tokenized, lang='en') new_cols = {"stars": int(stars)} for k in nested_feature_dict.keys(): new_dict = {} for nested_key in nested_feature_dict[k].keys(): new_cols[k + " " + nested_key] = nested_feature_dict[k][nested_key] df = pd.DataFrame(new_cols, index=[0]) remove_cols = [ 'readability grades Kincaid', 'readability grades ARI', 'readability grades FleschReadingEase', 'sentence info characters_per_word', 'sentence info syll_per_word', 'sentence info words_per_sentence', 'sentence info characters', 'sentence info syllables', 'sentence info long_words' ] df.drop(columns=remove_cols, inplace=True) def get_tag(pos_tag): if pos_tag.startswith('J'): return wordnet.ADJ elif pos_tag.startswith('R'): return wordnet.ADV elif pos_tag.startswith('V'): return wordnet.VERB else: return wordnet.NOUN def clean_review(review): # lower case and remove special characters\whitespaces review = re.sub(r'[^a-zA-Z\s]', '', review, re.I | re.A) review = review.lower() review = review.strip() # tokenize document tokens = wpt.tokenize(review) # filter stopwords out of document review = [token for token in tokens if token not in stop_words] #get POS tags for the review pos_tags = pos_tag(review) # lemmatize review review = [ WordNetLemmatizer().lemmatize(t[0], get_tag(t[1])) for t in pos_tags ] # re-create document from filtered tokens review = ' '.join(review) return review sid = SentimentIntensityAnalyzer() sentiments = sid.polarity_scores(text) sentiments_df = json_normalize(sentiments) df["compound"] = sentiments_df["compound"] df_scaled = scaler.transform(df) df_transformed = pca_transformer.transform(df_scaled) prediction = rf_model.predict( df_transformed) # runs globally loaded model on the data print(prediction) return render_template('home.html', predicted=round(np.exp(prediction[0]), 2)) return render_template('home.html', predicted='Error')
def __init__(self, text): tokenized = '\n\n'.join('\n'.join(' '.join(token.value for token in sentence) for sentence in paragraph) for paragraph in segmenter.analyze(text)) self.results = readability.getmeasures(tokenized, lang='en')