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from functools import lru_cache
import attr
import pandas as pd
import numpy as np
import spacy
from nltk.tokenize.texttiling import TextTilingTokenizer
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer
@lru_cache
def load_sentence_transformer(model_name='all-MiniLM-L6-v2'):
"""
all_MiniLM_L6_v2 - offline
all-MiniLM-L6-v2 - Online
"""
model = SentenceTransformer(model_name)
return model
@lru_cache
def load_spacy():
return spacy.load('en_core_web_sm')
model = load_sentence_transformer()
nlp = load_spacy()
@attr.s
class SemanticTextSegmentation:
"""
Segment a call transcript based on topics discussed in the call using
TextTilling with Sentence Similarity via sentence transformer.
Paramters
---------
data: pd.Dataframe
Pass the trascript in the dataframe format
utterance: str
pass the column name which represent utterance in transcript dataframe
"""
data = attr.ib()
utterance = attr.ib(default='utterance')
def __attrs_post_init__(self):
columns = self.data.columns.tolist()
def get_segments(self, threshold=0.7):
"""
returns the transcript segments computed with texttiling and sentence-transformer.
Paramters
---------
threshold: float
sentence similarity threshold. (used to merge the sentences into coherant segments)
Return
------
new_segments: list
list of segments
"""
segments = self._text_tilling()
merge_index = self._merge_segments(segments, threshold)
new_segments = []
for i in merge_index:
seg = ' '.join([segments[_] for _ in i])
new_segments.append(seg)
return new_segments
def _merge_segments(self, segments, threshold):
segment_map = [0]
for index, (text1, text2) in enumerate(zip(segments[:-1], segments[1:])):
sim = self._get_similarity(text1, text2)
if sim >= threshold:
segment_map.append(0)
else:
segment_map.append(1)
return self._index_mapping(segment_map)
def _index_mapping(self, segment_map):
index_list = []
temp = []
for index, i in enumerate(segment_map):
if i == 1:
index_list.append(temp)
temp = [index]
else:
temp.append(index)
index_list.append(temp)
return index_list
def _get_similarity(self, text1, text2):
sentence_1 = [i.text.strip()
for i in nlp(text1).sents if len(i.text.split(' ')) > 1]
sentence_2 = [i.text.strip()
for i in nlp(text2).sents if len(i.text.split(' ')) > 2]
embeding_1 = model.encode(sentence_1)
embeding_2 = model.encode(sentence_2)
embeding_1 = np.mean(embeding_1, axis=0).reshape(1, -1)
embeding_2 = np.mean(embeding_2, axis=0).reshape(1, -1)
sim = cosine_similarity(embeding_1, embeding_2)
return sim
def _text_tilling(self):
tt = TextTilingTokenizer(w=15, k=10)
text = '\n\n\t'.join(self.data[self.utterance].tolist())
segment = tt.tokenize(text)
segment = [i.replace("\n\n\t", ' ') for i in segment]
return segment |