Normalizing Social Media Texts by Combining Word Embeddings and Edit Distances in a Random Forest Regressor
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1 Normalizing Social Media Texts by Combining Word Embeddings and Edit Distances in a Random Forest Regressor Rob van der Goot r.van.der.goot@rug.nl Rob van der Goot r.van.der.goot@rug.nl / 31
2 1 Problem 2 Error Detection 3 Generation 4 Ranking 5 Conclusion 6 Future Work Rob van der Goot r.van.der.goot@rug.nl / 31
3 Outline 1 Problem 2 Error Detection 3 Generation 4 Ranking 5 Conclusion 6 Future Work Rob van der Goot r.van.der.goot@rug.nl / 31
4 Problem Adapt Natural Language Processing pipelines to noisy (web) data Rob van der Goot / 31
5 Problem Adapt Natural Language Processing pipelines to noisy (web) data Normalize Rob van der Goot / 31
6 Problem Spelling Correction vs. Normalization spell. corr. normalization Rob van der Goot / 31
7 Problem Spelling Correction abilites teh kingdon abilities the kingdom Rob van der Goot / 31
8 Problem Normalization abilites abilities teh the kingdon kingdom doin doing Bham Birmingham 2 to The There ggggrrrreeeeeaaaaaaattttttt great ur your Rob van der Goot r.van.der.goot@rug.nl / 31
9 Problem Traditional spelling correction framework: Error detection Candidate generation Ranking of candidates Rob van der Goot / 31
10 Problem Train set: 2,577 tweets from (Li and Liu 2014) Test set: LexNorm (Han and Baldwin 2011) 549 tweets Rob van der Goot / 31
11 Outline 1 Problem 2 Error Detection 3 Generation 4 Ranking 5 Conclusion 6 Future Work Rob van der Goot r.van.der.goot@rug.nl / 31
12 Error Detection Spelling correction: Dictionary lookup Rob van der Goot / 31
13 Error Detection Often skipped in normalization methods Here as well, because the goal is to be used in a pipeline All tokens are considered to be a possible error/disfluency Recall = 100% But the original word is always kept! Rob van der Goot r.van.der.goot@rug.nl / 31
14 Outline 1 Problem 2 Error Detection 3 Generation 4 Ranking 5 Conclusion 6 Future Work Rob van der Goot r.van.der.goot@rug.nl / 31
15 Generation Spelling correction: Lexical edit distance Phonetic edit distance (Double Metaphone) Rob van der Goot / 31
16 Generation Spelling correction: Lexical edit distance Phonetic edit distance (Double Metaphone) Good results Rob van der Goot / 31
17 Generation Spelling correction: Lexical edit distance Phonetic edit distance (Double Metaphone) Good results So we use an existing system (Aspell) Rob van der Goot / 31
18 Generation Other disfluencies A more data aware model is necessary Rob van der Goot r.van.der.goot@rug.nl / 31
19 Generation Other disfluencies A more data aware model is necessary Semi-supervised Rob van der Goot r.van.der.goot@rug.nl / 31
20 Generation Other disfluencies A more data aware model is necessary Semi-supervised Word Embeddings Rob van der Goot r.van.der.goot@rug.nl / 31
21 Generation Word Embeddings Model taken from (Godin et al. 2015) Trained on 400 million Tweets 3,039,345 words Use cosine distance to find top-n words in vector-space Rob van der Goot / 31
22 Generation Rob van der Goot / 31
23 Outline 1 Problem 2 Error Detection 3 Generation 4 Ranking 5 Conclusion 6 Future Work Rob van der Goot r.van.der.goot@rug.nl / 31
24 Ranking Spelling correction: Combination of edit distances Rob van der Goot / 31
25 Ranking Previous approaches: Ngram based approaches Combine Ranking with generation Rob van der Goot / 31
26 Ranking My approach: Use features from generation Supplement these features with N-Gram features Google Ngrams 1 & Twitter Ngrams 2 Combine all features in a Random Forest Classifier Default parameters Scikit Learn, except for the number of trees = Brants and Franz Herdağdelen 2013 Rob van der Goot r.van.der.goot@rug.nl / 31
27 Ranking Rob van der Goot / 31
28 Ranking Ranking (ablation) Rob van der Goot / 31
29 Ranking Ranking System top1 top3 top10 top20 upper bound (Li and Liu 2012) (Li and Liu 2014) (Li and Liu 2015) Our system Rob van der Goot / 31
30 Outline 1 Problem 2 Error Detection 3 Generation 4 Ranking 5 Conclusion 6 Future Work Rob van der Goot r.van.der.goot@rug.nl / 31
31 Conclusion Overview Aspell spell. corr. My approach normalization Rob van der Goot / 31
32 Conclusion For the normalization task: Word embeddings complement edit distances well A random forest classifier works very well for ranking This is a simple system, with a reasonable performance Rob van der Goot r.van.der.goot@rug.nl / 31
33 Outline 1 Problem 2 Error Detection 3 Generation 4 Ranking 5 Conclusion 6 Future Work Rob van der Goot r.van.der.goot@rug.nl / 31
34 Future Work Multilingual/multiword embeddings Generation (build own language models) Rob van der Goot / 31
35 Future Work Multilingual/multiword embeddings Generation (build own language models) Parameter tuning, add domain specific information Find candidate with: word.* Rob van der Goot / 31
36 Future Work This system was created for use in a pipeline system Rob van der Goot r.van.der.goot@rug.nl / 31
37 Future Work This system was created for use in a pipeline system Parse a word graph based on the output of this normalization Rob van der Goot r.van.der.goot@rug.nl / 31
38 Future Work Rob van der Goot / 31
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