Add alias() function

README.md

@@ -111,6 +111,28 @@ from kss import split_sentences
 output = split_sentences("YOUR_INPUT_STRING", **kwargs)
 ```
 
+### 6. Alias of module names
+Because there are so many modules in Kss, user may have difficulty remembering the names of each module.
+Kss provides aliases for some modules to make it easier to use them.
+```python
+from kss import Kss
+
+module_1 = Kss("split_morphemes")
+module_2 = Kss("tokenize")
+# For example, 'split_morphemes' module can be loaded by using the alias named 'tokenize'.
+```
+
+You can check the alias of each module by using the `alias()` function.
+```python
+from kss import Kss
+
+Kss.alias()
+```
+
+```python
+{'aug': 'augment', 'augmentation': 'augment', 'collocation': 'collocate', 'hangulization': 'hangulize', 'hangulisation': 'hangulize', 'hangulise': 'hangulize', 'hanja': 'hanja2hangul', 'hangul2jamo': 'h2j', 'hangul2hcj': 'h2hcj', 'jamo2hangul': 'j2h', 'jamo2hcj': 'j2hcj', 'hcj2hangul': 'hcj2h', 'hcj2jamo': 'hcj2j', 'josa': 'select_josa', 'keyword': 'extract_keywords', 'keywords': 'extract_keywords', 'morpheme': 'split_morphemes', 'morphemes': 'split_morphemes', 'annonymization': 'anonymize', 'news_cleaning': 'clean_news', 'news': 'clean_news', 'completed_form': 'is_completed_form', 'completed': 'is_completed_form', 'filter': 'filter_out', 'reduce_repeats': 'reduce_char_repeats', 'reduce_char': 'reduce_char_repeats', 'reduce_chars': 'reduce_char_repeats', 'reduce_emoticon': 'reduce_emoticon_repeats', 'reduce_emoticons': 'reduce_emoticon_repeats', 'reduce_emo': 'reduce_emoticon_repeats', 'remove_invisible': 'remove_invisible_chars', 'invisible_chars': 'remove_invisible_chars', 'invisible': 'remove_invisible_chars', 'normalization': 'normalize', 'normalisation': 'normalize', 'normalise': 'normalize', 'preprocessing': 'preprocess', 'prep': 'preprocess', 'romanization': 'romanize', 'romanisation': 'romanize', 'romanise': 'romanize', 'safety': 'is_unsafe', 'check_safety': 'is_unsafe', 'sentence': 'split_sentences', 'sentences': 'split_sentences', 'sent_split': 'split_sentences', 'sent_splits': 'split_sentences', 'sents_split': 'split_sentences', 'split_sent': 'split_sentences', 'split_sents': 'split_sentences', 'spacing': 'correct_spacing', 'space': 'correct_spacing', 'spaces': 'correct_spacing', 'summarization': 'summarize_sentences', 'summarize': 'summarize_sentences', 'summ': 'summarize_sentences', 'morph': 'split_morphemes', 'morphs': 'split_morphemes', 'tokenize': 'split_morphemes', 'tokenization': 'split_morphemes', 'split_morph': 'split_morphemes', 'split_morphs': 'split_morphemes', 'morph_split': 'split_morphemes', 'morph_splits': 'split_morphemes', 'morphs_split': 'split_morphemes'}
+```
+
 ## Supported Modules
 Kss supports the following modules and there are the simple usages of each module in the following sections.
 
@@ -186,7 +208,7 @@ Args:
 - text (`Union[str, List[str], Tuple[str]]`): single text or list of texts
 - descriptive (`bool`): return descriptive pronunciation, the 'descriptive' means a real-life pronunciation
 - group_vowels (`bool`): If True, the vowels of the identical sound are normalized. (e.g. ㅒ -> ㅖ)
-- to_syllable: If True, hangul letters or jamo are assembled to form syllables.
+- to_syllable (`bool`): If True, hangul letters or jamo are assembled to form syllables.
 - convert_english_to_hangul_phonemes (`bool`): If True, convert English to Hangul phonemes
 - convert_numbers_to_hangul_phonemes (`bool`): If True, convert numbers to Hangul phonemes
 - num_workers (`Union[int, str]`): the number of multiprocessing workers
@@ -691,6 +713,9 @@ Args:
 - noun_only (`bool`): whether to extract only nouns or not
 - num_workers (`Union[int, str]`): the number of multiprocessing workers
 
+Returns:
+- `Union[List[str], List[Tuple[str, float]]]`: list of keywords or list of tuples of keywords and scores
+
 Examples:
 ```python
 >>> from kss import Kss

kss/__init__.py

@@ -100,8 +100,12 @@
     "filter": "filter_out",
     "reduce_repeats": "reduce_char_repeats",
     "reduce_char": "reduce_char_repeats",
+    "reduce_chars": "reduce_char_repeats",
     "reduce_emoticon": "reduce_emoticon_repeats",
+    "reduce_emoticons": "reduce_emoticon_repeats",
+    "reduce_emo": "reduce_emoticon_repeats",
     "remove_invisible": "remove_invisible_chars",
+    "invisible_chars": "remove_invisible_chars",
     "invisible": "remove_invisible_chars",
     "normalization": "normalize",
     "normalisation": "normalize",
@@ -117,12 +121,24 @@
     "sentences": "split_sentences",
     "sent_split": "split_sentences",
     "sent_splits": "split_sentences",
+    "sents_split": "split_sentences",
+    "split_sent": "split_sentences",
+    "split_sents": "split_sentences",
     "spacing": "correct_spacing",
     "space": "correct_spacing",
     "spaces": "correct_spacing",
     "summarization": "summarize_sentences",
     "summarize": "summarize_sentences",
     "summ": "summarize_sentences",
+    "morph": "split_morphemes",
+    "morphs": "split_morphemes",
+    "tokenize": "split_morphemes",
+    "tokenization": "split_morphemes",
+    "split_morph": "split_morphemes",
+    "split_morphs": "split_morphemes",
+    "morph_split": "split_morphemes",
+    "morph_splits": "split_morphemes",
+    "morphs_split": "split_morphemes",
 }
 
 
@@ -140,6 +156,10 @@ def help(self):
     def available():
         return list(supported_modules.keys())
 
+    @staticmethod
+    def alias():
+        return alias
+
     def _check_module(self, module: str, supported_modules, alias):
         from kss._utils.sanity_checks import _check_type
 
@@ -181,4 +201,4 @@ def _find_closest_module(module, min_distance=0.5):
 
 
 __ALL__ = list(supported_modules.keys()) + ["Kss"]
-__version__ = "6.0.1"
+__version__ = "6.0.2"

kss/_modules/g2p/g2p.py

@@ -54,7 +54,7 @@ def g2p(
         text (Union[str, List[str], Tuple[str]]): single text or list of texts
         descriptive (bool): return descriptive pronunciation, the 'descriptive' means a real-life pronunciation
         group_vowels (bool): If True, the vowels of the identical sound are normalized. (e.g. ㅒ -> ㅖ)
-        to_syllable: If True, hangul letters or jamo are assembled to form syllables.
+        to_syllable (bool): If True, hangul letters or jamo are assembled to form syllables.
         convert_english_to_hangul_phonemes (bool): If True, convert English to Hangul phonemes
         convert_numbers_to_hangul_phonemes (bool): If True, convert numbers to Hangul phonemes
         num_workers (Union[int, str]): the number of multiprocessing workers

kss/_modules/keywords/extract_keywords.py

@@ -1,6 +1,6 @@
 # This code was copied from KR-WordRank [https://github.com/lovit/KR-WordRank]
 # And modified by Hyunwoong Ko [https://github.com/hyuwoongko]
-from typing import List, Union
+from typing import List, Union, Tuple
 
 from kss._modules.keywords.utils import KRWordRank
 from kss._utils.sanity_checks import _check_text, _check_type, _check_backend_mecab_pecab_only
@@ -15,7 +15,7 @@ def extract_keywords(
     backend: str = "auto",
     noun_only: bool = True,
     num_workers: Union[int, str] = "auto",
-):
+) -> Union[List[str], List[Tuple[str, float]]]:
     """
     This extracts keywords from the given text.
     This uses TextRank algorithm to extract keywords.
@@ -30,6 +30,9 @@ def extract_keywords(
         noun_only (bool): whether to extract only nouns or not
         num_workers (Union[int, str]): the number of multiprocessing workers
 
+    Returns:
+        Union[List[str], List[Tuple[str, float]]]: list of keywords or list of tuples of keywords and scores
+
     Examples:
         >>> from kss import Kss
         >>> extract_keywords = Kss("extract_keywords")
@@ -50,8 +53,8 @@ def extract_keywords(
         >>> print(output)
         ['너무', '정말', '마지막', '영화', '음악']
 
-        References:
-            This was copied from [KR-WordRank](https://github.com/lovit/KR-WordRank) and modified by Kss
+    References:
+        This was copied from [KR-WordRank](https://github.com/lovit/KR-WordRank) and modified by Kss
     """
 
     text, finish = _check_text(text)