# # Copyright (c) 2013-present, Anoop Kunchukuttan # All rights reserved. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. # #Program for sentence splitting of Indian language input # # @author Anoop Kunchukuttan # """ Sentence splitter for Indian languages. Contains a rule-based sentence splitter that can understand common non-breaking phrases in many Indian languages. """ import re from indicnlp.transliterate import unicode_transliterate from indicnlp import langinfo ## for language which have danda as delimiter ## period is not part of the sentence delimiters DELIM_PAT_DANDA=re.compile(r'[\?!\u0964\u0965]') ## for languages which don't have danda as delimiter DELIM_PAT_NO_DANDA=re.compile(r'[\.\?!\u0964\u0965\uAAF1\uAAF0\uABEB\uABEC\uABED\uABEE\uABEF\u1C7E\u1C7F]') ## pattern to check for presence of danda in text CONTAINS_DANDA=re.compile(r'[\u0964\u0965]') ## pattern to check for presence of valid domain characters in text CONTAINS_VALID_DOMAIN_CHAR=re.compile(r'^[a-zA-Z0-9_-]$') ## pattern to check for presence of multiple consecutive spaces in text CONTAINS_MULTIPLE_SPACES = re.compile(' +') def is_latin_or_numeric(character): """ Check if a character is a Latin character (uppercase or lowercase) or a number. Parameters: character (str): The character to be checked. Returns: bool: True if the character is a Latin character or a number, False otherwise. """ return re.match(CONTAINS_VALID_DOMAIN_CHAR, character) is not None def is_acronym_abbvr(text,lang): """Is the text a non-breaking phrase Args: text (str): text to check for non-breaking phrase lang (str): ISO 639-2 language code Returns: boolean: true if `text` is a non-breaking phrase """ ack_chars = { ## acronym for latin characters 'ए', 'ऎ', 'बी', 'बि', 'सी', 'सि', 'डी', 'डि', 'ई', 'इ', 'एफ', 'ऎफ', 'जी', 'जि', 'एच','ऎच', 'आई', 'आइ','ऐ', 'जे', 'जॆ', 'के', 'कॆ', 'एल', 'ऎल', 'एम','ऎम', 'एन','ऎन', 'ओ', 'ऒ', 'पी', 'पि', 'क्यू', 'क्यु', 'आर', 'एस','ऎस', 'टी', 'टि', 'यू', 'यु', 'वी', 'वि', 'व्ही', 'व्हि', 'डब्ल्यू', 'डब्ल्यु', 'एक्स','ऎक्स', 'वाय', 'जेड', 'ज़ेड', ## add halant to the previous English character mappings. 'एफ्', 'ऎफ्', 'एच्', 'ऎच्', 'एल्', 'ऎल्', 'एम्', 'ऎम्', 'एन्', 'ऎन्', 'आर्', 'एस्', 'ऎस्', 'एक्स्', 'ऎक्स्', 'वाय्', 'जेड्', 'ज़ेड्', #Indic vowels 'ऄ', 'अ', 'आ', 'इ', 'ई', 'उ', 'ऊ', 'ऋ', 'ऌ', 'ऍ', 'ऎ', 'ए', 'ऐ', 'ऑ', 'ऒ', 'ओ', 'औ', 'ॠ', 'ॡ', #Indic consonants 'क', 'ख', 'ग', 'घ', 'ङ', 'च', 'छ', 'ज', 'झ', 'ञ', 'ट', 'ठ', 'ड', 'ढ', 'ण', 'त', 'थ', 'द', 'ध', 'न', 'ऩ', 'प', 'फ', 'ब', 'भ', 'म', 'य', 'र', 'ऱ', 'ल', 'ळ', 'ऴ', 'व', 'श', 'ष', 'स', 'ह', ## abbreviation 'श्री', 'डॉ', 'कु', 'चि', 'सौ', } return unicode_transliterate.UnicodeIndicTransliterator.transliterate(text,lang,'hi') in ack_chars def sentence_split(text,lang,delim_pat='auto'): ## New signature """split the text into sentences A rule-based sentence splitter for Indian languages written in Brahmi-derived scripts. The text is split at sentence delimiter boundaries. The delimiters can be configured by passing appropriate parameters. The sentence splitter can identify non-breaking phrases like single letter, common abbreviations/honorofics for some Indian languages. Args: text (str): text to split into sentence lang (str): ISO 639-2 language code delim_pat (str): regular expression to identify sentence delimiter characters. If set to 'auto', the delimiter pattern is chosen automatically based on the language and text. Returns: list: list of sentences identified from the input text """ if lang == "ur": from urduhack.tokenization import sentence_tokenizer if len(text.split())<2: sentences = text.split() else: sentences = sentence_tokenizer(text) return sentences #print('Input: {}'.format(delim_pat)) if delim_pat=='auto': if langinfo.is_danda_delim(lang): # in modern texts it is possible that period is used as delimeter # instead of DANDA. Hence, a check. Use danda delimiter pattern # only if text contains at least one danda if CONTAINS_DANDA.search(text) is None: delim_pat=DELIM_PAT_NO_DANDA #print('LANG has danda delim. TEXT_CONTAINS_DANDA: FALSE --> DELIM_PAT_NO_DANDA') else: delim_pat=DELIM_PAT_DANDA #print('LANG has danda delim. TEXT_CONTAINS_DANDA: TRUE --> DELIM_PAT_DANDA') else: delim_pat=DELIM_PAT_NO_DANDA #print('LANG has no danda delim --> DELIM_PAT_NO_DANDA') ## otherwise, assume the caller set the delimiter pattern ### Phase 1: break on sentence delimiters. cand_sentences=[] begin=0 text = text.strip() for mo in delim_pat.finditer(text): p1=mo.start() p2=mo.end() ## NEW if p1>0 and text[p1-1].isnumeric(): continue ## Prevents splitting on "." in URLs/emails in indic texts. if lang != "en": if is_latin_or_numeric(text[p1-1]): if p1+1 < len(text) and is_latin_or_numeric(text[p1+1]): continue end=p1+1 s= text[begin:end].strip() if len(s)>0: cand_sentences.append(s) begin=p1+1 s= text[begin:].strip() if len(s)>0: cand_sentences.append(s) if not delim_pat.search('.'): ## run phase 2 only if delimiter pattern contains period #print('No need to run phase2') return cand_sentences # print(cand_sentences) # print('====') # return cand_sentences ### Phase 2: Address the fact that '.' may not always be a sentence delimiter ### Method: If there is a run of lines containing only a word (optionally) and '.', ### merge these lines as well one sentence preceding and succeeding this run of lines. final_sentences=[] sen_buffer='' bad_state=False for i, sentence in enumerate(cand_sentences): words=sentence.split(' ') #if len(words)<=2 and words[-1]=='.': if len(words)==1 and sentence[-1]=='.': bad_state=True sen_buffer = sen_buffer + ' ' + sentence ## NEW condition elif sentence[-1]=='.' and is_acronym_abbvr(words[-1][:-1],lang): if len(sen_buffer)>0 and not bad_state: final_sentences.append(sen_buffer) sen_buffer = sentence else: sen_buffer = sen_buffer + ' ' + sentence bad_state=True elif bad_state: sen_buffer = sen_buffer + ' ' + sentence if len(sen_buffer)>0: final_sentences.append(sen_buffer) sen_buffer='' bad_state=False else: ## good state if len(sen_buffer)>0: final_sentences.append(sen_buffer) sen_buffer=sentence bad_state=False if len(sen_buffer)>0: final_sentences.append(sen_buffer) for i in range(0, len(final_sentences)): final_sentences[i] = CONTAINS_MULTIPLE_SPACES.sub(' ', final_sentences[i].strip()) return final_sentences