lti Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
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1 Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments Kevin Gimpel, Nathan Schneider, Brendan O'Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, and Noah A. Smith
2 Why does this paper have so many authors?
3 Why does this paper have so many authors? Our goal: Build a Twitter part-of-speech tagger in one day
4 Plan: Large team of annotators Simple, carefully-designed annotation scheme Features leveraging existing resources (treebanks) and unannotated data
5 Plan: Large team of annotators Simple, carefully-designed annotation scheme Features leveraging existing resources (treebanks) and unannotated data Outcome: Tag set for Twitter 1,827 annotated English tweets POS tagger with ~90% accuracy Didn t finish in a day, but took < 250 person-hours Available to download!
6 The Data
7
8 non-standard spellings mu-word abbreviations hashtags Also: at-mentions, URLs, emoticons, symbols, typos, etc.
9 Tag Set
10 Start with coarse set of Penn Treebank tags Add Twitter-specific tags
11 Coarse treebank tags: common noun proper noun pronoun verb adjective adverb punctuation determiner preposition verb particle coordinating conjunction numeral interjection predeterminer / existential there
12 Coarse treebank tags: common noun proper noun pronoun verb adjective adverb punctuation determiner preposition verb particle coordinating conjunction numeral interjection predeterminer / existential there
13 Penn Treebank tokenization is unsuitable for OMG ur from PA? i am too (: where abouts? you re I m going ima get me a flip phone for real
14 Penn Treebank tokenization is unsuitable for OMG ur from PA? i am too (: where abouts? you re I m going ima get me a flip phone for real Solution: Don t try to tokenize these Instead, introduce compound tags
15 Penn Treebank tokenization is unsuitable for Twitter: OMG ur from PA? i am too (: where abouts? you re I m going ima get me a flip phone for real nominal+verbal Solution: Don t try to tokenize these Instead, introduce compound tags
16 Twitter-specific tags: hashtag at-mention URL / address emoticon Twitter discourse marker other (mu-word abbreviations, symbols, garbage)
17 Twitter-specific tags: hashtag at-mention URL / address emoticon Twitter discourse marker other (mu-word abbreviations, symbols, garbage)
18 Hashtags Twitter hashtags are sometimes used as ordinary words (35% of the time) and other times as topic markers Innovative, but traditional, too! Another fun one to watch on the #ipad! #utcd2 #utpol #tcot
19 Hashtags Twitter hashtags are sometimes used as ordinary words (35% of the time) and other times as topic markers proper noun Innovative, but traditional, too! Another fun one to watch on the #ipad! #utcd2 #utpol #tcot hashtag We only use hashtag for topic markers
20 Twitter Discourse Marker Retweet construction: : I never bought candy bars from those kids on my doorstep so I guess they re all in gangs now.
21 Twitter Discourse Marker Retweet construction: : I never bought candy bars from those kids on my doorstep so I guess they re all in gangs now. Twitter discourse marker
22 Twitter Discourse Marker Retweet construction: : I never bought candy bars from those kids on my doorstep so I guess they re all in gangs now. Twitter discourse marker : LMBO! This man filed an EMERGENCY Motion for Continuance on account of the Rangers game tonight! Wow lmao
23 Twitter Discourse Marker Retweet construction: : I never bought candy bars from those kids on my doorstep so I guess they re all in gangs now. Twitter discourse marker : LMBO! This man filed an EMERGENCY Motion for Continuance on account of the Rangers game tonight! Wow lmao
24 Resung tag set: 25 tags
25 Annotation
26 17 researchers from Carnegie Mellon Each spent 2-20 hours annotating Annotators corrected output of Stanford tagger Penn Treebank consulted for difficult cases
27 Two annotators corrected and standardized annotations from the original 17 annotators A third annotator tagged a sample of the tweets from scratch Inter-annotator agreement: 92.2% Cohen s kappa: One annotator made a single final pass through the data, correcting errors and improving consistency
28 Experiments
29 Experimental Setup 1,827 annotated tweets 1,000 for training 327 for development 500 for testing (OOV rate: 30%) Systems: Stanford tagger (retrained on our data) Our own baseline CRF tagger Our tagger augmented with Twitter-specific features
30 Results Stanford Tagger Our tagger, base features Our tagger, all features Inter-annotator agreement
31 Results Stanford Tagger Our tagger, base features Our tagger, all features Inter-annotator agreement
32 Twitter Orthographic Features Regular expressions to detect at-mentions, hashtags, and URLs With Without
33 Distributional Similarity Features Embeddings in a lowdimensional space based on neighboring words Computed using 134k unannotated tweets 86 With Without
34 Phonetic Normalization Features 91 Metaphone algorithm (Philips, 1990) maps tokens to equivalence classes based on phonetics Examples: tomarrow tommorow tomorr tomorrow tomorrowwww hahaaha hahaha hahahah hahahahhaa hehehe hehehee With Without thangs thanks thanksss thanx things thinks thnx knew kno know knw n nah naw new no noo nooooooo now
35 Tag Dictionary Features One feature for each tag a word occurs with in the Penn Treebank, with its frequency rank A similar feature for Metaphone classes of Penn Treebank words 86 With Without
36 Conclusions We developed a tag set, annotated data, designed features, and trained models Case study in rapidly porting a fundamental NLP task to a social media domain Data may be useful for domain adaptation or semi-supervised learning
37 Thanks! Tagger, tokenizer, and annotations are available (50+ downloads already!):
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