Skip to content

Commit 6e6b850

Browse files
subalalithaflsubalalithafl
committed
Spell correction
1 parent 4c65832 commit 6e6b850

18 files changed

+271
-269
lines changed

‎dist/firstlanguage_python-2.2.tar.gz

41 KB
Binary file not shown.

‎doc/controllers/basic-ap-is.md

+110-110
Original file line numberDiff line numberDiff line change
@@ -10,47 +10,47 @@ basic_ap_is_controller = client.basic_ap_is
1010

1111
## Methods
1212

13-
* [Get Stemmer](../../doc/controllers/basic-ap-is.md#get-stemmer)
14-
* [Get Lemma](../../doc/controllers/basic-ap-is.md#get-lemma)
15-
* [Get Morph](../../doc/controllers/basic-ap-is.md#get-morph)
13+
* [Get Stemmer](../../doc/controllers/basic-ap-is.md#get-stemmer)
14+
* [Get Lemma](../../doc/controllers/basic-ap-is.md#get-lemma)
15+
* [Get Morph](../../doc/controllers/basic-ap-is.md#get-morph)
1616
* [Get Postag](../../doc/controllers/basic-ap-is.md#get-postag)
1717

1818

1919
# Get Stemmer
2020

21-
A word takes different inflectional forms. For instance, the word, "Compute" can take the forms, "computing", "computation", and "computerize". The NLP applications such as Search Engines and Information Extraction would want to store the base or stem of the word, i.e "Compute" instead of accomodating all its inflected forms. This will yield in dimensionality reduction and incerases the efficiency of the system. The stemmer cuts the prefix and suffix of a word.
22-
23-
# Languages covered:
24-
25-
Our stemmer works for the following 26 languages.
26-
27-
| Languages | ISO Code |
28-
|--------------|------------|
29-
| Arabic | ar |
30-
| Catalan | ca |
31-
| Danish | da |
32-
| German | de |
33-
| Greek | el |
34-
| English | en |
35-
| Spanish | es |
36-
| Basque | eu |
37-
| Finnish | fi |
38-
| French | fr |
39-
| Irish | ga |
40-
| Hindi | hi |
41-
| Hungarian | hu |
42-
| Indonesian | id |
43-
| Italian | it |
44-
| Lithuanian | lt |
45-
| Nepali | ne |
46-
| Dutch | nl |
47-
| Norwegian | no |
48-
| Portuguese | pt |
49-
| Romanian | ro |
50-
| Russian | ru |
51-
| Serbian | sr |
52-
| Swedish | sv |
53-
| Tamil | ta |
21+
A word takes different inflectional forms. For instance, the word, "Compute" can take the forms, "computing", "computation", and "computerize". The NLP applications such as Search Engines and Information Extraction would want to store the base or stem of the word, i.e "Compute" instead of accomodating all its inflected forms. This will yield in dimensionality reduction and incerases the efficiency of the system. The stemmer cuts the prefix and suffix of a word.
22+
23+
# Languages covered:
24+
25+
Our stemmer works for the following 26 languages.
26+
27+
| Languages | ISO Code |
28+
|--------------|------------|
29+
| Arabic | ar |
30+
| Catalan | ca |
31+
| Danish | da |
32+
| German | de |
33+
| Greek | el |
34+
| English | en |
35+
| Spanish | es |
36+
| Basque | eu |
37+
| Finnish | fi |
38+
| French | fr |
39+
| Irish | ga |
40+
| Hindi | hi |
41+
| Hungarian | hu |
42+
| Indonesian | id |
43+
| Italian | it |
44+
| Lithuanian | lt |
45+
| Nepali | ne |
46+
| Dutch | nl |
47+
| Norwegian | no |
48+
| Portuguese | pt |
49+
| Romanian | ro |
50+
| Russian | ru |
51+
| Serbian | sr |
52+
| Swedish | sv |
53+
| Tamil | ta |
5454
| Turkish | tr |
5555

5656
```python
@@ -71,7 +71,7 @@ def get_stemmer(self,
7171
## Example Usage
7272

7373
```python
74-
body = jsonpickle.decode('{"$$__case":0,"$$__case_of":"oneOf","value":{"input":{"text":"அவள் வேகமாக ஓடினாள்","lang":"ta"}}}')
74+
body = jsonpickle.decode('{"$$__case":0,"$$__case_of":"oneOf","value":{"input":{"text":"அவள் வேகமாக ஓடினாள்","lang":"ta"}}}')
7575

7676
result = basic_ap_is_controller.get_stemmer(body)
7777
```
@@ -81,15 +81,15 @@ result = basic_ap_is_controller.get_stemmer(body)
8181
```json
8282
[
8383
{
84-
"orginalText": "அவள்",
84+
"originalText": "அவள்",
8585
"stem": "அவள்"
8686
},
8787
{
88-
"orginalText": "வேகமாக",
88+
"originalText": "வேகமாக",
8989
"stem": "வேகம்"
9090
},
9191
{
92-
"orginalText": "ஓடினாள்",
92+
"originalText": "ஓடினாள்",
9393
"stem": "ஓடி"
9494
}
9595
]
@@ -106,27 +106,27 @@ result = basic_ap_is_controller.get_stemmer(body)
106106

107107
# Get Lemma
108108

109-
Lemmatizer is similar to stemmer that gives the stemmed version of a word but lemmatizer differs from the stemmer in giving a meaningful stem or the lemma. For instance, for the word, "smiling", the stemmer would give, "smil", stemming the suffix, "ing" but the lemmatizer would give the meaningful stem, "smile". lemmatizers can be used in applications such as, Machine Translation, Search Engines, Text Summarization etc.
110-
111-
# Languages covered:
112-
113-
| Languages | ISO Code |
114-
|--------------------|----------|
115-
| Catalan | ca |
116-
| Danish | da |
117-
| Dutch | nl |
118-
| English | en |
119-
| French | fr |
120-
| German | de |
121-
| Greek | el |
122-
| Italian | it |
123-
| Lithuanian | lt |
124-
| Macedonian | mk |
125-
| Norwegian (Bokmål) | nb |
126-
| Polish | pl |
127-
| Portuguese | pt |
128-
| Romanian | ro |
129-
| Russian | ru |
109+
Lemmatizer is similar to stemmer that gives the stemmed version of a word but lemmatizer differs from the stemmer in giving a meaningful stem or the lemma. For instance, for the word, "smiling", the stemmer would give, "smil", stemming the suffix, "ing" but the lemmatizer would give the meaningful stem, "smile". lemmatizers can be used in applications such as, Machine Translation, Search Engines, Text Summarization etc.
110+
111+
# Languages covered:
112+
113+
| Languages | ISO Code |
114+
|--------------------|----------|
115+
| Catalan | ca |
116+
| Danish | da |
117+
| Dutch | nl |
118+
| English | en |
119+
| French | fr |
120+
| German | de |
121+
| Greek | el |
122+
| Italian | it |
123+
| Lithuanian | lt |
124+
| Macedonian | mk |
125+
| Norwegian (Bokmål) | nb |
126+
| Polish | pl |
127+
| Portuguese | pt |
128+
| Romanian | ro |
129+
| Russian | ru |
130130
| Spanish | es |
131131

132132
```python
@@ -147,7 +147,7 @@ def get_lemma(self,
147147
## Example Usage
148148

149149
```python
150-
body = jsonpickle.decode('{"$$__case":0,"$$__case_of":"oneOf","value":{"input":{"text":"Smiling makes everyone happy","lang":"en"}}}')
150+
body = jsonpickle.decode('{"$$__case":0,"$$__case_of":"oneOf","value":{"input":{"text":"Smiling makes everyone happy","lang":"en"}}}')
151151

152152
result = basic_ap_is_controller.get_lemma(body)
153153
```
@@ -157,19 +157,19 @@ result = basic_ap_is_controller.get_lemma(body)
157157
```json
158158
[
159159
{
160-
"orginalText": "Smiling",
160+
"originalText": "Smiling",
161161
"lemmatized": "smile"
162162
},
163163
{
164-
"orginalText": "makes",
164+
"originalText": "makes",
165165
"lemmatized": "make"
166166
},
167167
{
168-
"orginalText": "everyone",
168+
"originalText": "everyone",
169169
"lemmatized": "everyone"
170170
},
171171
{
172-
"orginalText": "happy",
172+
"originalText": "happy",
173173
"lemmatized": "happy"
174174
}
175175
]
@@ -186,27 +186,27 @@ result = basic_ap_is_controller.get_lemma(body)
186186

187187
# Get Morph
188188

189-
Morphological Analyzer analyzes how a word is formed. It breaks a word into smaller units called, "morphemes" and gives a clue on the pattern of words of a particular langauge. It can be used for building applications such as, Machine Translation, Text Summarization, Search systems etc.
190-
191-
# Languages covered:
192-
193-
| Languages | ISO Code |
194-
|--------------------|----------|
195-
| Catalan | ca |
196-
| Danish | da |
197-
| Dutch | nl |
198-
| English | en |
199-
| French | fr |
200-
| German | de |
201-
| Greek | el |
202-
| Italian | it |
203-
| Japanese | ja |
204-
| Lithuanian | lt |
205-
| Macedonian | mk |
206-
| Norwegian (Bokmål) | nb |
207-
| Polish | pl |
208-
| Portuguese | pt |
209-
| Russian | ru |
189+
Morphological Analyzer analyzes how a word is formed. It breaks a word into smaller units called, "morphemes" and gives a clue on the pattern of words of a particular langauge. It can be used for building applications such as, Machine Translation, Text Summarization, Search systems etc.
190+
191+
# Languages covered:
192+
193+
| Languages | ISO Code |
194+
|--------------------|----------|
195+
| Catalan | ca |
196+
| Danish | da |
197+
| Dutch | nl |
198+
| English | en |
199+
| French | fr |
200+
| German | de |
201+
| Greek | el |
202+
| Italian | it |
203+
| Japanese | ja |
204+
| Lithuanian | lt |
205+
| Macedonian | mk |
206+
| Norwegian (Bokmål) | nb |
207+
| Polish | pl |
208+
| Portuguese | pt |
209+
| Russian | ru |
210210
| Spanish | es |
211211

212212
```python
@@ -227,7 +227,7 @@ def get_morph(self,
227227
## Example Usage
228228

229229
```python
230-
body = jsonpickle.decode('{"$$__case":0,"$$__case_of":"oneOf","value":{"input":{"text":"Let us begin the API development.","lang":"en"}}}')
230+
body = jsonpickle.decode('{"$$__case":0,"$$__case_of":"oneOf","value":{"input":{"text":"Let us begin the API development.","lang":"en"}}}')
231231

232232
result = basic_ap_is_controller.get_morph(body)
233233
```
@@ -411,20 +411,20 @@ result = basic_ap_is_controller.get_morph(body)
411411

412412
# Get Postag
413413

414-
Parts of Speech Tagger, which is shortly known as POS Tagger is a software that automatically finds the word classes, when a text input is given. The text input can be a word, a sentence or a set of sentences. The word classes are the grammatical categories such as, Noun, Verb, Adverb etc. The category assigned to each word is called as a tag. A set of tags, each indicating a grammatical category is called, "tagsets". POS tagging is a mandatory pre-processing for most of the Natural Language Processing Applications such as, Information Extraction, Information Retreival systems and Summary generation systems. A POS Tagger is a language-dependent software as the grammar rules will differ for every language. For instance, a word ending with "ing" might indicate a "Verb" in English but this will not be applicable for other languages.
415-
416-
# Languages covered:
417-
418-
| Languages | ISO Code |
419-
|--------------------|----------|
420-
| Chinese | zh |
421-
| Dutch | nl |
422-
| English | en |
423-
| German | de |
424-
| Italian | it |
425-
| Lithuanian | lt |
426-
| Polish | pl |
427-
| Romanian | ro |
414+
Parts of Speech Tagger, which is shortly known as POS Tagger is a software that automatically finds the word classes, when a text input is given. The text input can be a word, a sentence or a set of sentences. The word classes are the grammatical categories such as, Noun, Verb, Adverb etc. The category assigned to each word is called as a tag. A set of tags, each indicating a grammatical category is called, "tagsets". POS tagging is a mandatory pre-processing for most of the Natural Language Processing Applications such as, Information Extraction, Information Retreival systems and Summary generation systems. A POS Tagger is a language-dependent software as the grammar rules will differ for every language. For instance, a word ending with "ing" might indicate a "Verb" in English but this will not be applicable for other languages.
415+
416+
# Languages covered:
417+
418+
| Languages | ISO Code |
419+
|--------------------|----------|
420+
| Chinese | zh |
421+
| Dutch | nl |
422+
| English | en |
423+
| German | de |
424+
| Italian | it |
425+
| Lithuanian | lt |
426+
| Polish | pl |
427+
| Romanian | ro |
428428
| Tamil | ta |
429429

430430
```python
@@ -445,7 +445,7 @@ def get_postag(self,
445445
## Example Usage
446446

447447
```python
448-
body = jsonpickle.decode('{"$$__case":0,"$$__case_of":"oneOf","value":{"input":{"text":"Let us begin the API development.","lang":"en"}}}')
448+
body = jsonpickle.decode('{"$$__case":0,"$$__case_of":"oneOf","value":{"input":{"text":"Let us begin the API development.","lang":"en"}}}')
449449

450450
result = basic_ap_is_controller.get_postag(body)
451451
```
@@ -455,31 +455,31 @@ result = basic_ap_is_controller.get_postag(body)
455455
```json
456456
[
457457
{
458-
"orginalText": "Let",
458+
"originalText": "Let",
459459
"postag": "VERB"
460460
},
461461
{
462-
"orginalText": "us",
462+
"originalText": "us",
463463
"postag": "PRON"
464464
},
465465
{
466-
"orginalText": "begin",
466+
"originalText": "begin",
467467
"postag": "VERB"
468468
},
469469
{
470-
"orginalText": "the",
470+
"originalText": "the",
471471
"postag": "DET"
472472
},
473473
{
474-
"orginalText": "API",
474+
"originalText": "API",
475475
"postag": "PROPN"
476476
},
477477
{
478-
"orginalText": "development",
478+
"originalText": "development",
479479
"postag": "NOUN"
480480
},
481481
{
482-
"orginalText": ".",
482+
"originalText": ".",
483483
"postag": "PUNCT"
484484
}
485485
]

0 commit comments

Comments
 (0)