Princeton_workshop_presentation.pdf

Transcript

1. 1 Crash Course for NLP with
2. 2 Apple ORG
 U.K. GPE
 $1 billion MONEY Output import print spacy nlp = spacy. ( )
 doc = nlp( )

 ent doc.ents:
 (ent.text, ent.label_) load for in “en_core_web_sm“ """Apple is looking at buying U.K. startup for $1 billion""" Matthew Honnibal CTO, Founder Ines Montani CEO, Founder Explosion
3. 3 About this presentation Akos Kadar Machine Learning Engineer Victoria Slocum Developer Advocate Live Demo Case study #2 Pipelines for entities Slide 15-21 Case study #1 Rulers & NER Slide 4-14 Intro to spaCy and components Slide 23 Wrap up and questions
4. 4 Natural Language Understanding Document Tokens Attributes Break down into smaller meaningful pieces. Predict/Assign properties to individual tokens, groups of tokens and whole document. In a nutshell Categorize texts, extract spans of interest and relations between them Lemmas, sentence boundaries, parts-of-speech, syntax, etc. Examples Information Extraction Linguistic
 Analysis
5. 5 spaCy and it’s components spaCy provides a modular architecture to construct NLP pipelines that are can be tailored towards individual needs. Text Doc tagger parser ... spaCy pipeline tokenizer
6. 6 The processing pipeline object, contains all the different components. The nlp object import spacy nlp = spacy. ( ) # Create a blank English nlp object
 blank "en" The Doc lets you access information about the text in a structured way, and no information is lost. The doc object # process a string of text with the nlp object
 # Iterate over tokens in a Doc
 doc = nlp( )

 token doc:
 (token.text) "Hello world!" for in print Hello
 world
 ! Output Objects in spaCy
7. 7 Represent the tokens in a document – for example, a word or a punctuation character. The token object # Index into the Doc to get a single Token
 # Get the token text via the .text attribute
 token = doc[ ]

 (token.text) 1 print A slice of the document consisting of one or more tokens. Doesn’t contain data. The span object # A slice from the Doc is a Span object
 # Get the span text via the .text attribute
 span = doc[ : ]

 (span.text) 1 3 print Objects in spaCy world Output world! Output
8. 8 These attributes are also called lexical attributes: they refer to the entry in the vocabulary and don't depend on the token's context. doc = nlp( ) ( , [token.i token doc]) ( , [token.text token doc]) ( , [token.is_alpha token doc]) ( , [token.is_punct token doc]) ( , [token.like_num token doc]) "It costs $5." "Index: " "Text: " "is_alpha:" "is_punct:" "like_num:" print print print print print for in for in for in for in for in Lexical Attributes Index: [0, 1, 2, 3, 4]
 Text: ['It', 'costs', '$', '5', '.']

 is_alpha: [True, True, False, False, False]
 is_punct: [False, False, False, False, True]
 like_num: [False, False, False, True, False] Output
9. 9 Trained pipelines import spacy nlp = spacy. ( ) # Load a trained pipeline
 load "en_core_web_sm" $ python -m spacy download en_core_web_sm Bash e Models that enable spaCy to predict linguistic attributes in contexˆ e Trained on annotated example texth e Can be updated with more examples to fine-tune predictions Assigns part-of-speech tags to tokens. tagger Machine Learning Analyzes syntactic structure and assigns dependency relations between tokens. parser Machine Learning Identifies non-overlapping named entities. ner Machine Learning
10. 10 Let's take a look at the model's predictions. In this example, we're using spaCy to predict part-of-speech tags, the word types in context. import print spacy

 nlp = spacy. ( )

 doc = nlp( )

 token doc:
 (token.text, token.pos_) # Load the small English pipeline
 # Process a text
 # Iterate over the tokens
 # Print the text and the predicted part-of-speech tag
 load for in "en_core_web_sm" "She ate the pizza" Predicting POS tags She PRON
 ate VERB
 the DET
 pizza NOUN Output
11. 11 In addition to the part-of- speech tags, we can also predict how the words are related. For example, whether a word is the subject of the sentence or an object. doc = nlp( )

 token doc:
 (token.text, token.pos_, token.dep_, token.head.text) "She ate the pizza" # Iterate over the tokens
 for in print Predicting syntactic dependencies She PRON nsubj ate
 ate VERB ROOT ate
 the DET det pizza
 pizza NOUN dobj ate Output
12. 12 Named entities are "real world objects" that are assigned a name – for example, a person, an organization or a country. The doc.ents property lets you access the named entities predicted by the named entity recognition model. # Process a text
 # Iterate over the predicted entities
 # Print the entity text and its label
 doc = nlp( )

 ent doc.ents:
 (ent.text, ent.label_) "Apple is looking at buying U.K. startup for $1 billion" for in print Predicting named entities Apple ORG
 U.K. GPE
 $1 billion MONEY Output
13. 13 Unlike named entities, which have clear token boundaries and are often comprised of the same syntactic units, spans can be overlapping and composed of arbitrary phrases. The doc.spans property lets you access the predicted spans. import from import spacy spacy.tokens Span text nlp spacy.blank( ) doc nlp(text) doc.spans[ ] [ Span(doc, , , ), Span(doc, , , ), ] = = = = "Welcome to the Bank of China." "en" "sc" "ORG" "GPE" 3 6 5 6 Predicting spans
14. 14 Assigns base forms to tokens. lemmatizer rule-based & ML doc = (“ “) doc[0].lemma_ “ “ doc[1].lemma_ “ “ nlp Apples are great. apple be assert == assert == Predicts categories over a whole document. textcat Machine Learning doc = (“ “) doc.cats[“ “] 1.0 nlp Apples are great. positive assert == Custom sentence boundary detection logic without dependency parsing. sentencizer Machine Learning nlp nlp .add_pipe( )
 doc = ( )
 len(list(doc.sents)) 2 "sentencizer" "This is a sentence. This is another sentence." assert == Other pipeline components
15. 15 https://blog.victoriaslocum.com/posts/spanruler_ner_data spaCy and Prodigy workflow for doing Named Entity Recognition with the addition of a Ruler component to improve scores. Recent blog post Case study #1 Predicting Named Entities from restaurant reviews
16. 16 $ python -m spacy project run ... Bash Contains commands, assets, and other information project.yml $ python -m spacy project document -o README.md Bash Contains information from project.yml for GitHub, auto-generated README.md $ python -m spacy project assets Bash Contains all data for the project, can be downloaded through command assets All scripts related to the project, for data processing, evaluation, or other scripts Configs for model training, pipeline settings like components and scoring configs spaCy project system Allows us to share the end-to-end workflow and orchestrate training, packaging and serving within a single, reproducible system. $ python -m spacy project clone ... Bash
17. 17 B-Rating 2
 I-Rating start
 O restaurants
 O with
 B-Amenity inside
 I-Amenity dining IOB format original data 'spans': [{'end': 7,
 'label': 'Rating',
 'start': 0,
 'token_end': 1,
 'token_start': 0},
 {'end': 38,
 'label': 'Amenity',
 'start': 25,
 'token_end': 5,
 'token_start': 4}],
 'text': '2 start restaurants with inside dining',
 ... JSONL format processed data (Liu, et al, 2013) to determine non-overlapping entities such as Rating, Location, Restaurant_Name, Price, Dish, Amenity, and Cuisine in restaurant reviews. MIT Restaurant Reviews What’s the data like?
18. 18 Evaluate Precision: How accurate the predictions are whenever the model predicts something Recall: How often the model predicts something F-score: Overall model score $ python -m spacy train configs/ner.cfg --paths.train corpus/train.spacy --paths.dev corpus/dev.spacy --paths.vectors en_core_web_lg --output training/ner/ Train model NER model Annotated data
19. 19 { "label": "Rating", "pattern": [ {"LOWER": "at", "OP": "?"}, {"LOWER": "least", "OP": "?"}, {"IS_DIGIT": True}, {"LOWER": {"REGEX": "star(s)?"}}, {"LOWER": {"REGEX": "rat(ed|ing|ings)?"}, "OP": "?"}, ], }, Rule-based matching RATING with at least 3 stars RATING with no 1 star ratings RATING rated 4 stars
20. 20 Writing your own rules { "label": "Location", "pattern": [ {"LOWER": "less"}, {"LOWER": "than"}, {"IS_DIGIT": True}, {"LOWER": {"REGEX": "mile(s)?"}}, {"LOWER": "from", "OP": "?"}, {"LOWER": "here", "OP": "?"}, ], }, LOCATION find me a chinese restaurant less than 4 miles LOCATION where is a good indian restaurant less than 1 mile from here Each pattern is set with a label, the same set of labels the NER model uses. We then have a dictionary of tokens to match. You can find the list of available operators and token attributes for matching in the docs, but be aware of the required pipeline components for each attribute.
21. 21 We can run spacy project assemble to assemble the ner_ruler pipeline. If you look at the command, you can see that it’s using spacy assemble and specifying the source for the NER and Tok2Vec components, as well as the python rules file. Text Doc NER SpanRuler NER + SpanRuler tokenizer Putting it together - name: "assemble-review" help: "Assemble trained NER pipeline with SpanRuler with reviewed data." script: - >- python -m spacy assemble configs/ner_ruler_review.cfg models/ner_ruler_review --components.tok2vec.source training/ner_review/model-best/ --components.ner.source training/ner_review/model-best/ --code scripts/rules_review.py
22. 22 https://github.com/explosion/princetondh/tree/master/litbank_pipeline We’ll go through the project a bit, train a tagger model, and leave you with some exercises to work through later as well. Demo time! Case study #2 Training ML models with the spaCy project system An annotated dataset of 100 works of English- language fiction for NLP and the computational humanities. We’ll focus on named entities and events LitBank dataset
23. 23 Thanks so much for being here! 8 (blog.) .co3 8 twitter.com/ 8 linkedin.com/in/ 8 @explosion.ai victoriaslocum victorialslocu3 victorialslocu3 victoria 8 .ai/bloh 8 youtube.com/@ 8 twitter.com/ explosion ExplosionAy spacy_io 8 twitter.com/ 8 @explosion.ai kadarako• akos