university of sheffield, nlp twitie: an open-source information extraction pipeline for microblog...
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University of Sheffield, NLP
TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text
Kalina BontchevaLeon Derczynski
Adam FunkMark A. Greenwood
Diana MaynardNiraj Aswani
© The University of Sheffield, 1995-2013This work is licensed underthe Creative Commons Attribution-NonCommercial-NoDerivs Licence
University of Sheffield, NLP
The Problem
• Running ANNIE on 300 news articles – 87% f-score
• Running ANNIE on some tweets - < 40% f-score
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Example: Persons in news articles
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Example: Persons in tweets
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Genre Differences in Entity Types
News Tweets
PER Politicians, business leaders, journalists, celebrities
Sportsmen, actors, TV personalities, celebrities, names of friends
LOC Countries, cities, rivers, and other places related to current affairs
Restaurants, bars, local landmarks/areas, cities, rarely countries
ORG Public and private companies, government organisations
Bands, internet companies, sports clubs
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Tweet-specific NER challenges
• Capitalisation is not indicative of named entities
• All uppercase, e.g. APPLE IS AWSOME
• All lowercase, e.g. all welcome, joe included
• All letters upper initial, e.g. 10 Quotes from Amy Poehler That Will Get You Through High School
• Unusual spelling, acronyms, and abbreviations
• Social media conventions:
• Hashtags, e.g. #ukuncut, #RusselBrand, #taxavoidance
• @Mentions, e.g. @edchi (PER), @mcg_graz (LOC), @BBC (ORG)
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TwitIE: GATE’s new Twitter NER pipeline
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Importing tweets into GATE
• GATE now supports JSON format import for tweets
• Located in the Format_Twitter plugin
• Automatically used for files *.json
• Alternatively, specify text/x-json-twitter as a mime type
• The tweet text becomes the document, all other JSON fields become features
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Language Detection: Less than 50% English
The main challenges on tweets/Facebook status updates:
the short number of tokens (10 tokens/tweet on average)
the noisy nature of the words (abbreviations, misspellings).
Due to the length of the text, we can make the assumption that one tweet is written in only one language
We have adapted the TextCat language identification plugin
Provided fingerprints for 5 languages: DE, EN, FR, ES, NL
You can extend it to new languages easily
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Language Detection Examples
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Tokenisation
Splitting a text into its constituent parts
Plenty of “unusual”, but very important tokens in social media:
– @Apple – mentions of company/brand/person names
– #fail, #SteveJobs – hashtags expressing sentiment, person or company names
– :-(, :-), :-P – emoticons (punctuation and optionally letters)
– URLs
Tokenisation key for entity recognition and opinion mining
A study of 1.1 million tweets: 26% of English tweets have a URL, 16.6% - a hashtag, and 54.8% - a user name mention [Carter, 2013].
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Example
– #WiredBizCon #nike vp said when @Apple saw what http://nikeplus.com did, #SteveJobs was like wow I didn't expect this at all.
– Tokenising on white space doesn't work that well:
• Nike and Apple are company names, but if we have tokens such as #nike and @Apple, this will make the entity recognition harder, as it will need to look at sub-token level
– Tokenising on white space and punctuation characters doesn't work well either: URLs get separated (http, nikeplus), as are emoticons and email addresses
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The TwitIE Tokeniser
Treat RTs and URLs as 1 token each
#nike is two tokens (# and nike) plus a separate annotation HashTag covering both. Same for @mentions -> UserID
Capitalisation is preserved, but an orthography feature is added: all caps, lowercase, mixCase
Date and phone number normalisation, lowercasing, and emoticons are optionally done later in separate modules
Consequently, tokenisation is faster and more generic
Also, more tailored to our NER module
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POS Tagging
• The accuracy of the Stanford POS tagger drops from about 97% on news to 80% on tweets (Ritter, 2011)
• Need for an adapted POS tagger, specifically for tweets
• We re-trained the Stanford POS tagger using some hand-annotated tweets, IRC and news texts
• Next we compare the differences between the ANNIE POS Tagger and the Tweet POS Tagger on the example tweets
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POS Tagging Example
• TwitIE POS tagger on the left
• ANNIE POS tagger on the right
• The TwitIE POS tagger is a separate paper at RANLP’2013
• Beats Ritter (2011); uses a grown-up tag set (cf. Gimpel, 2011)
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Tweet Normalisation
“RT @Bthompson WRITEZ: @libbyabrego honored?! Everybody knows the libster is nice with it...lol...(thankkkks a bunch;))”
OMG! I’m so guilty!!! Sprained biibii’s leg! ARGHHHHHH!!!!!!
Similar to SMS normalisation
For some components to work well (POS tagger, parser), it is necessary to produce a normalised version of each token
BUT uppercasing, and letter and exclamation mark repetition often convey strong sentiment
Therefore some choose not to normalise, while others keep both versions of the tokens
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A normalised example
Normaliser currently based on spelling correction and some lists of common abbreviations
Outstanding issues:
Insert new Token annotations, so easier to POS tag, etc? For example: “trying to” now 1 annotation
Some abbreviations which span token boundaries (e.g. gr8, do n’t) difficult to handle
Capitalisation and punctuation normalisation
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TwitIE NER Results
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Trying TwitIE
• Plugin in the latest GATE snapshot and forthcoming 7.2 release
• Download details at: https://gate.ac.uk/wiki/twitie.html
• Available soon as a web service on the forthcoming AnnoMarket NLP cloud marketplace:
• https://annomarket.com/
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Coming Soon: TwitIE-as-a-Service
Preview of some text analytics services on AnnoMarket.com
University of Sheffield, NLP
Acknowledgements
• Kalina Bontcheva is supported by a Career Acceleration Fellowship from the Engineering and Physical Sciences Research Council (grant EP/I004327/1)
• This research is also partially supported by the EU-funded FP7 TrendMiner project (http://www.trendminer-project.eu) and the CHIST-ERA uComp project (http://www.ucomp.eu)
Thank you for your time!