Introducing Natural Language
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- Virginia Bradley
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1 Session #WWDC18 Introducing Natural Language 713 Doug Davidson, Senior Software Engineer Vivek Kumar Rangarajan Sridhar, Software Engineering Manager 2018 Apple Inc. All rights reserved. Redistribution or public display not permitted without written permission from Apple.
2
3 Natural Intelligence Language Input Typed text Transcribed speech Recognized handwriting
4 NSLinguisticTagger Language Identification Tokenization Word Sentence Paragraph Natural Language Input Part of Speech Intelligence Lemmatization Named Entity Recognition Linguistics Machine Learning
5 What s new in NLP?
6 Natural Language Framework
7 Swift APIs for NLP
8 Swift APIs for NLP Custom NLP models
9 Swift APIs for NLP Custom NLP models Performance
10 Swift APIs for NLP Custom NLP models Performance Privacy
11 Natural Language
12 Natural Language Language Identification Tokenization Word Sentence Paragraph Natural Intelligence Language Input Part of Speech Lemmatization Named Entity Recognition Linguistics Machine Learning
13
14 < John M Design is coming together but needs some final tweaks It s getting late and I m tired. Will pick it up tomorrow morning good night!
15 < John M Design is coming together but needs some final tweaks It s getting late and I m tired. Will pick it up tomorrow morning good night!
16 < John M < John M Design is coming together but needs some final tweaks Design is coming together but needs some final tweaks It s getting late and I m tired. 困死啦睡觉去了了 Will pick it up tomorrow morning good night!
17 < John M < John M Design is coming together but needs some final tweaks Design is coming together but needs some final tweaks It s getting late and I m tired. 困死啦睡觉去了了 Will pick it up tomorrow morning good night!
18 Language Identification import NaturalLanguage let recognizer = NLLanguageRecognizer() recognizer.processstring( 困死啦睡觉去了了 ) let lang = recognizer.dominantlanguage let hypotheses = recognizer.languagehypotheses(withmaximum:2)
19 Language Identification import NaturalLanguage let recognizer = NLLanguageRecognizer() recognizer.processstring( 困死啦睡觉去了了 ) let lang = recognizer.dominantlanguage let hypotheses = recognizer.languagehypotheses(withmaximum:2)
20 Language Identification import NaturalLanguage let recognizer = NLLanguageRecognizer() recognizer.processstring( 困死啦睡觉去了了 ) let lang = recognizer.dominantlanguage let hypotheses = recognizer.languagehypotheses(withmaximum:2)
21 Language Identification import NaturalLanguage let recognizer = NLLanguageRecognizer() recognizer.processstring( 困死啦睡觉去了了 ) let lang = recognizer.dominantlanguage let hypotheses = recognizer.languagehypotheses(withmaximum:2)
22 Language Identification import NaturalLanguage let recognizer = NLLanguageRecognizer() recognizer.processstring( 困死啦睡觉去了了 ) let lang = recognizer.dominantlanguage let hypotheses = recognizer.languagehypotheses(withmaximum:2) zh-hans: Simplified Chinese
23 Language Identification import NaturalLanguage let recognizer = NLLanguageRecognizer() recognizer.processstring( 困死啦睡觉去了了 ) let lang = recognizer.dominantlanguage let hypotheses = recognizer.languagehypotheses(withmaximum:2) zh-hans: Simplified Chinese
24 Tokenization import NaturalLanguage let tokenizer = NLTokenizer(unit:.word) let str = 困死啦睡觉去了了 let strrange = str.startindex..< str.endindex tokenizer.string = str let tokenarray = tokenizer.tokens(for: strrange)
25 Tokenization import NaturalLanguage let tokenizer = NLTokenizer(unit:.word) let str = 困死啦睡觉去了了 let strrange = str.startindex..< str.endindex tokenizer.string = str let tokenarray = tokenizer.tokens(for: strrange)
26 Tokenization import NaturalLanguage let tokenizer = NLTokenizer(unit:.word) let str = 困死啦睡觉去了了 let strrange = str.startindex..< str.endindex tokenizer.string = str let tokenarray = tokenizer.tokens(for: strrange)
27 Tokenization import NaturalLanguage let tokenizer = NLTokenizer(unit:.word) let str = 困死啦睡觉去了了 let strrange = str.startindex..< str.endindex tokenizer.string = str let tokenarray = tokenizer.tokens(for: strrange)
28 Tokenization import NaturalLanguage let tokenizer = NLTokenizer(unit:.word) let str = 困死啦睡觉去了了 let strrange = str.startindex..< str.endindex tokenizer.string = str let tokenarray = tokenizer.tokens(for: strrange)
29 Tokenization import NaturalLanguage let tokenizer = NLTokenizer(unit:.word) let str = 困死啦睡觉去了了 let strrange = str.startindex..< str.endindex tokenizer.string = str let tokenarray = tokenizer.tokens(for: strrange) 困死啦睡觉去了了
30 Tokenization import NaturalLanguage let tokenizer = NLTokenizer(unit:.word) let str = 困死啦睡觉去了了 let strrange = str.startindex..< str.endindex tokenizer.string = str let tokenarray = tokenizer.tokens(for: strrange) 困死啦睡觉去了了
31 < John M Design is coming together but needs some final tweaks 困死啦睡觉去了了
32
33 Harry Harry Potter Harry Styles Harry Connick Jr.
34 Named Entity Recognition import NaturalLanguage let tagger = NLTagger(tagSchemes: [.nametype]) let str = Prince Harry and Meghan Markle had their wedding ceremony in Windsor let strrange = str.startindex..< str.endindex tagger.string = str tagger.setlanguage(.english, range: strrange) let tags = tagger.tags(in: strrange, unit:.word, scheme:.nametype, options:.omitwhitespace)
35 Named Entity Recognition import NaturalLanguage let tagger = NLTagger(tagSchemes: [.nametype]) let str = Prince Harry and Meghan Markle had their wedding ceremony in Windsor let strrange = str.startindex..< str.endindex tagger.string = str tagger.setlanguage(.english, range: strrange) let tags = tagger.tags(in: strrange, unit:.word, scheme:.nametype, options:.omitwhitespace)
36 Named Entity Recognition import NaturalLanguage let tagger = NLTagger(tagSchemes: [.nametype]) let str = Prince Harry and Meghan Markle had their wedding ceremony in Windsor let strrange = str.startindex..< str.endindex tagger.string = str tagger.setlanguage(.english, range: strrange) let tags = tagger.tags(in: strrange, unit:.word, scheme:.nametype, options:.omitwhitespace)
37 Named Entity Recognition import NaturalLanguage let tagger = NLTagger(tagSchemes: [.nametype]) let str = Prince Harry and Meghan Markle had their wedding ceremony in Windsor let strrange = str.startindex..< str.endindex tagger.string = str tagger.setlanguage(.english, range: strrange) let tags = tagger.tags(in: strrange, unit:.word, scheme:.nametype, options:.omitwhitespace)
38 Named Entity Recognition import NaturalLanguage let tagger = NLTagger(tagSchemes: [.nametype]) let str = Prince Harry and Meghan Markle had their wedding ceremony in Windsor let strrange = str.startindex..< str.endindex tagger.string = str tagger.setlanguage(.english, range: strrange) let tags = tagger.tags(in: strrange, unit:.word, scheme:.nametype, options:.omitwhitespace)
39 Named Entity Recognition import NaturalLanguage let tagger = NLTagger(tagSchemes: [.nametype]) let str = Prince Harry and Meghan Markle had their wedding ceremony in Windsor let strrange = str.startindex..< str.endindex tagger.string = str tagger.setlanguage(.english, range: strrange) let tags = tagger.tags(in: strrange, unit:.word, scheme:.nametype, options:.omitwhitespace) PER: Prince Harry LOC: Windsor Meghan Markle
40 Natural Language Developer documentation Future support for new NLP APIs
41 Natural Intelligence Language Input
42 Swift APIs for NLP
43 Swift APIs for NLP Custom NLP models
44 Custom NLP Models Doug Davidson, Senior Software Engineer
45 Concept Learning
46 Concept Learning Examples
47 Concept Learning Examples
48 Concept Learning Classify Examples and Understand
49 Machine Learning Classify Training Data and Understand
50 Types of Custom Model
51 Types of Custom Model Text Classification Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum Label Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. Label Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. Label
52 Types of Custom Model Text Classification Word Tagging Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. Label Label Lorem ipsum dolor sit amet Label Label Label Label Label Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. Label
53 Text Classification Sentiment classification I am really excited, would definitely recommend it highly! POSITIVE This was terrible, much worse than I expected. NEGATIVE It was OK, something I could live with for now. NEUTRAL
54 Text Classification Topic classification Foles late TD pass leads Eagles to 1st Super Bowl title SPORTS Apple introduces new 9.7 inch ipad with Apple Pencil support TECHNOLOGY Apple Reports Second Quarter Results FINANCE
55 Text Classification Domain classification I m looking for a place to stay in Barcelona. HOTELS Where can I get good Mexican food on a Sunday? RESTAURANTS Find me an inexpensive round-trip flight to London. FLIGHTS
56 Word Tagging Part of speech Το παιδί διάβασε το βιβλίο DETERMINER NOUN VERB DETERMINER NOUN
57 Word Tagging Named entity The ipad is popular in Singapore NONE PRODUCT NONE NONE NONE LOCATION
58 Word Tagging Slot parsing Round trip fares from Pittsburgh to Philadelphia ROUND_TRIP NONE NONE FROM_LOCATION NONE TO_LOCATION
59 Word Tagging Chunking Tim Cook is the CEO of Apple Inc NP NP VP NP NP PP NP NP Noun phrase Verb phrase Prepositional phrase
60 Supervised Machine Learning Training and inference
61 Supervised Machine Learning Training and inference Annotated data Training
62 Supervised Machine Learning Training and inference Annotated data Training Inference User data Prediction
63 Custom NLP Models Training
64 Custom NLP Models Training Training Data
65 Custom NLP Models Training Training Data Create ML
66 Custom NLP Models Training Training Data Create ML Natural Language
67 Custom NLP Models Training Training Data Create ML Natural Language
68 Custom NLP Models Training Training Data Create ML Natural Language
69 Text Classifier Annotated data [ { text : I am really excited, would definitely recommend it highly!, label : Positive }, { text : It was OK, something I could live with for now., label : Neutral }, { text : This was terrible, much worse than I expected., label : Negative }, ]
70 Text Classifier Annotated data [ { text : I am really excited, would definitely recommend it highly!, label : Positive }, { text : It was OK, something I could live with for now., label : Neutral }, { text : This was terrible, much worse than I expected., label : Negative }, ]
71 Text Classifier Training import CreateML import Foundation let trainingdata = try MLDataTable(contentsOfFile: Bundle.main.url(forResource: news, withextension: json )!) let model = try MLTextClassifier(trainingWith: trainingdata, textcolumn: text, labelcolumn: label ) try model.write(to: URL(fileURLWithPath: /Users/me/Desktop/textclassifier.mlmodel )
72 Text Classifier Training import CreateML import Foundation let trainingdata = try MLDataTable(contentsOfFile: Bundle.main.url(forResource: news, withextension: json )!) let model = try MLTextClassifier(trainingWith: trainingdata, textcolumn: text, labelcolumn: label ) try model.write(to: URL(fileURLWithPath: /Users/me/Desktop/textclassifier.mlmodel )
73 Text Classifier Training import CreateML import Foundation let trainingdata = try MLDataTable(contentsOfFile: Bundle.main.url(forResource: news, withextension: json )!) let model = try MLTextClassifier(trainingWith: trainingdata, textcolumn: text, labelcolumn: label ) try model.write(to: URL(fileURLWithPath: /Users/me/Desktop/textclassifier.mlmodel )
74 Text Classifier Training import CreateML import Foundation let trainingdata = try MLDataTable(contentsOfFile: Bundle.main.url(forResource: news, withextension: json )!) let model = try MLTextClassifier(trainingWith: trainingdata, textcolumn: text, labelcolumn: label ) try model.write(to: URL(fileURLWithPath: /Users/me/Desktop/textclassifier.mlmodel )
75 Word Tagger Annotated data [ { tokens": [ AirPods, are, a, fantastic, product, from, Apple,. ], labels : [ PROD, NONE, NONE, NONE, NONE, NONE, ORG, NONE ] }, { tokens": [ Apple, and, Tim, Cook, have, another, hit,. ], labels : [ ORG, NONE, PER, PER, NONE, NONE, NONE, NONE ] } ]
76 Word Tagger Annotated data [ { tokens": [ AirPods, are, a, fantastic, product, from, Apple,. ], labels : [ PROD, NONE, NONE, NONE, NONE, NONE, ORG, NONE ] }, { tokens": [ Apple, and, Tim, Cook, have, another, hit,. ], labels : [ ORG, NONE, PER, PER, NONE, NONE, NONE, NONE ] } ]
77 Word Tagger Training import CreateML import Foundation let trainingdata = try MLDataTable(contentsOfFile: Bundle.main.url(forResource: products, withextension: json )!) let model = try MLWordTagger(trainingWith: trainingdata, tokenheader: tokens, labelcolumn: labels ) try model.write(to: URL(fileURLWithPath: /Users/me/Desktop/wordtagger.mlmodel ) Introducing Create ML Hall 2 Tuesday 2:00PM
78 Word Tagger Training import CreateML import Foundation let trainingdata = try MLDataTable(contentsOfFile: Bundle.main.url(forResource: products, withextension: json )!) let model = try MLWordTagger(trainingWith: trainingdata, tokenheader: tokens, labelcolumn: labels ) try model.write(to: URL(fileURLWithPath: /Users/me/Desktop/wordtagger.mlmodel ) Introducing Create ML Hall 2 Tuesday 2:00PM
79 Word Tagger Training import CreateML import Foundation let trainingdata = try MLDataTable(contentsOfFile: Bundle.main.url(forResource: products, withextension: json )!) let model = try MLWordTagger(trainingWith: trainingdata, tokenheader: tokens, labelcolumn: labels ) try model.write(to: URL(fileURLWithPath: /Users/me/Desktop/wordtagger.mlmodel ) Introducing Create ML Hall 2 Tuesday 2:00PM
80 Word Tagger Training import CreateML import Foundation let trainingdata = try MLDataTable(contentsOfFile: Bundle.main.url(forResource: products, withextension: json )!) let model = try MLWordTagger(trainingWith: trainingdata, tokenheader: tokens, labelcolumn: labels ) try model.write(to: URL(fileURLWithPath: /Users/me/Desktop/wordtagger.mlmodel ) Introducing Create ML Hall 2 Tuesday 2:00PM
81 Natural Language Inference What s New in Core ML, Part 1 Hall 1 Wednesday 9:00AM What s New in Core ML, Part 2 Hall 1 Wednesday 10:00AM
82 Natural Language Inference User data What s New in Core ML, Part 1 Hall 1 Wednesday 9:00AM What s New in Core ML, Part 2 Hall 1 Wednesday 10:00AM
83 Natural Language Inference User data Natural Language What s New in Core ML, Part 1 Hall 1 Wednesday 9:00AM What s New in Core ML, Part 2 Hall 1 Wednesday 10:00AM
84 Natural Language Inference User data Natural Language What s New in Core ML, Part 1 Hall 1 Wednesday 9:00AM What s New in Core ML, Part 2 Hall 1 Wednesday 10:00AM
85 Natural Language Inference User data Natural Language What s New in Core ML, Part 1 Hall 1 Wednesday 9:00AM What s New in Core ML, Part 2 Hall 1 Wednesday 10:00AM
86 Natural Language Inference User data Natural Language Label or Label sequence What s New in Core ML, Part 1 Hall 1 Wednesday 9:00AM What s New in Core ML, Part 2 Hall 1 Wednesday 10:00AM
87 Using a Custom Model import NaturalLanguage if let modelurl = Bundle.main.url(forResource: classifier, withextension: mlmodelc ) { let model = try NLModel(contentsOf: modelurl) let label = model.predictedlabel(for: I really loved it! ) }
88 Using a Custom Model import NaturalLanguage if let modelurl = Bundle.main.url(forResource: classifier, withextension: mlmodelc ) { let model = try NLModel(contentsOf: modelurl) let label = model.predictedlabel(for: I really loved it! ) }
89 Using a Custom Model import NaturalLanguage if let modelurl = Bundle.main.url(forResource: classifier, withextension: mlmodelc ) { let model = try NLModel(contentsOf: modelurl) let label = model.predictedlabel(for: I really loved it! ) }
90 Using a Custom Model import NaturalLanguage if let modelurl = Bundle.main.url(forResource: classifier, withextension: mlmodelc ) { let model = try NLModel(contentsOf: modelurl) let label = model.predictedlabel(for: I really loved it! ) }
91 Using Custom Models with NLTagger let mytagscheme = NLTagScheme( MyTagScheme ) let tagger = NLTagger(tagSchemes: [mytagscheme]) tagger.setmodels([model], fortagscheme: mytagscheme) tagger.string = mystring tagger.enumeratetags(in: range, unit:.sentence, scheme: mytagscheme, options: []) { (tag, tokenrange) -> Bool in // use the generated tags return true }
92 Using Custom Models with NLTagger let mytagscheme = NLTagScheme( MyTagScheme ) let tagger = NLTagger(tagSchemes: [mytagscheme]) tagger.setmodels([model], fortagscheme: mytagscheme) tagger.string = mystring tagger.enumeratetags(in: range, unit:.sentence, scheme: mytagscheme, options: []) { (tag, tokenrange) -> Bool in // use the generated tags return true }
93 Using Custom Models with NLTagger let mytagscheme = NLTagScheme( MyTagScheme ) let tagger = NLTagger(tagSchemes: [mytagscheme]) tagger.setmodels([model], fortagscheme: mytagscheme) tagger.string = mystring tagger.enumeratetags(in: range, unit:.sentence, scheme: mytagscheme, options: []) { (tag, tokenrange) -> Bool in // use the generated tags return true }
94 Using Custom Models with NLTagger let mytagscheme = NLTagScheme( MyTagScheme ) let tagger = NLTagger(tagSchemes: [mytagscheme]) tagger.setmodels([model], fortagscheme: mytagscheme) tagger.string = mystring tagger.enumeratetags(in: range, unit:.sentence, scheme: mytagscheme, options: []) { (tag, tokenrange) -> Bool in // use the generated tags return true }
95 Using Custom Models with NLTagger let mytagscheme = NLTagScheme( MyTagScheme ) let tagger = NLTagger(tagSchemes: [mytagscheme]) tagger.setmodels([model], fortagscheme: mytagscheme) tagger.string = mystring tagger.enumeratetags(in: range, unit:.sentence, scheme: mytagscheme, options: []) { (tag, tokenrange) -> Bool in // use the generated tags return true }
96 Using Custom Models with NLTagger let mytagscheme = NLTagScheme( MyTagScheme ) let tagger = NLTagger(tagSchemes: [mytagscheme]) tagger.setmodels([model], fortagscheme: mytagscheme) tagger.string = mystring tagger.enumeratetags(in: range, unit:.sentence, scheme: mytagscheme, options: []) { (tag, tokenrange) -> Bool in // use the generated tags return true }
97 Wade Bookmark organization Apple announces WWDC 2018 in Best way to cook an Apple Pie San Francisco Giants team news Golden State Warriors team news Finance: Apple stock page on NASD.. Healthy Kitchen: Whole grain meals WSJ business page Organize by topics, entities Wikipedia: San Francisco Giants Wikipedia: Golden State Warriors Wikipedia: Abraham Lincoln Wikipedia: Benjamin Franklin Wikipedia: Grand Canyon Trip planner: 10 things to see to LA Travelogue: Spending time in Oregon Great Barrier Reef: A trip across the P Hiking Shoes: The best choices in the
98 Demo
99 Swift APIs for NLP Custom NLP models
100 Swift APIs for NLP Custom NLP models Performance
101 Natural Language Available on all Apple platforms
102 Natural Language Available on all Apple platforms
103 Natural Language Available on all Apple platforms Standardized text processing
104 Text Processing Typical ML training Training Data Tokenization Machine CoreML Feature Extraction Learning Toolkit Convertor
105 Text Processing Typical ML inference User Data Output
106 Text Processing Typical ML inference User Data Output
107 Text Processing Typical ML inference Tokenization Feature Extraction User Data Output
108 Standarized Text Processing Natural Language
109 Standarized Text Processing Natural Language Training Data
110 Standarized Text Processing Natural Language Training Data Create ML
111 Standarized Text Processing Natural Language Training Data Create ML Natural Language Tokenization Feature Extraction Machine Learning
112 Standarized Text Processing Natural Language Training Data Create ML Natural Language Tokenization Feature Extraction Machine Learning
113 Standarized Text Processing Natural Language Training Data Create ML Natural Language Tokenization Feature Extraction Machine Learning
114 Standarized Text Processing Natural Language User Data Output
115 Standarized Text Processing Natural Language User Data Output
116 Standarized Text Processing Natural Language Natural Language User Data Output
117 Optimized Models Low latency Optimized for Apple hardware
118 Optimized Models Low latency Optimized for Apple hardware Open Source CRFSuite Natural Language Named Entity Recognition 70 MB 1.4 MB Chunking 30 MB 1.8 MB Results on CoNLL training and test data
119 ML Algorithms Text classification let modelparameters = MLTextClassifier.ModelParameters(algorithm:.maxEnt(version: 1)) let modelparameters = MLTextClassifier.ModelParameters(algorithm:.CRF(version: 1))
120 ML Algorithms Text classification let modelparameters = MLTextClassifier.ModelParameters(algorithm:.maxEnt(version: 1)) let modelparameters = MLTextClassifier.ModelParameters(algorithm:.CRF(version: 1))
121 ML Algorithms Text classification let modelparameters = MLTextClassifier.ModelParameters(algorithm:.maxEnt(version: 1)) let modelparameters = MLTextClassifier.ModelParameters(algorithm:.CRF(version: 1)) Maximum Entropy
122 ML Algorithms Text classification let modelparameters = MLTextClassifier.ModelParameters(algorithm:.maxEnt(version: 1)) let modelparameters = MLTextClassifier.ModelParameters(algorithm:.CRF(version: 1)) Maximum Entropy Conditional Random Field
123 ML Algorithms Text classification let modelparameters = MLTextClassifier.ModelParameters(algorithm:.maxEnt(version: 1)) let modelparameters = MLTextClassifier.ModelParameters(algorithm:.CRF(version: 1)) Maximum Entropy Conditional Random Field
124 ML Algorithms Word tagging let model = try MLWordTagger(trainingWith: trainingdata, tokenheader: tokens, labelcolumn: labels ) Conditional Random Field
125 ML Algorithms Word tagging let model = try MLWordTagger(trainingWith: trainingdata, tokenheader: tokens, labelcolumn: labels ) Conditional Random Field
126 Data
127 Data Validate training data Inspect training instances per class
128 Data
129 Data Training
130 Data Run different algorithms Training
131 Data Training
132 Data Evaluation Training
133 Data Set aside test data Test on out-of-domain data Evaluation Training
134 Data Evaluation Training
135 Data Evaluation Training
136 Data Fix by adding data Retrain model Evaluation Training
137 Data Workflow for ML Evaluation Training
138 Swift APIs for NLP Custom NLP models Performance
139 Swift APIs for NLP Custom NLP models Performance Privacy
140 Privacy Preserving ML Applied to NLP Natural Language Framework
141 Summary New: Natural Language Framework Custom NLP models Performance Privacy
142 More Information Machine Learning Get-Together Café Lounge Wednesday 6:15PM Natural Language Lab Technology Lab 4 Thursday 1:00PM
143
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