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University of Sheffield, NLP
TwitIE: An Open-Source Information Extraction
Pipeline for Microblog Text
Kalina Bontcheva
Le...
University of Sheffield, NLP
The Problem
• Running ANNIE on 300 news articles – 87% f-score
• Running ANNIE on some tweets...
University of Sheffield, NLP
Example: Persons in news articles
University of Sheffield, NLP
Example: Persons in tweets
University of Sheffield, NLP
Genre Differences in Entity Types
News Tweets
PER Politicians, business
leaders, journalists,...
University of Sheffield, NLP
Tweet-specific NER challenges
• Capitalisation is not indicative of named entities
• All uppe...
University of Sheffield, NLP
TwitIE: GATE’s new Twitter NER pipeline
University of Sheffield, NLP
Importing tweets into GATE
• GATE now supports JSON format import for tweets
• Located in the...
University of Sheffield, NLP
Language Detection: Less than 50% English
 The main challenges on tweets/Facebook status upd...
University of Sheffield, NLP
Language Detection Examples
University of Sheffield, NLP
Tokenisation
 Splitting a text into its constituent parts
 Plenty of “unusual”, but very im...
University of Sheffield, NLP
Example
– #WiredBizCon #nike vp said when @Apple saw what
http://nikeplus.com did, #SteveJobs...
University of Sheffield, NLP
The TwitIE Tokeniser
Treat RTs and URLs as 1 token each
#nike is two tokens (# and nike) pl...
University of Sheffield, NLP
POS Tagging
• The accuracy of the Stanford POS tagger drops from about
97% on news to 80% on ...
University of Sheffield, NLP
POS Tagging Example
• TwitIE POS tagger on the left
• ANNIE POS tagger on the right
• The Twi...
University of Sheffield, NLP
Tweet Normalisation
 “RT @Bthompson WRITEZ: @libbyabrego honored?!
Everybody knows the libst...
University of Sheffield, NLP
A normalised example
 Normaliser currently based on spelling correction and some
lists of co...
University of Sheffield, NLP
TwitIE NER Results
University of Sheffield, NLP
Trying TwitIE
• Plugin in the latest GATE snapshot and forthcoming 7.2
release
• Download det...
University of Sheffield, NLP
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...
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TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text

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Code: http://gate.ac.uk/wiki/twitie.html

Paper: https://gate.ac.uk/sale/ranlp2013/twitie/twitie-ranlp2013.pdf

Twitter is the largest source of microblog text, responsible for gigabytes of human discourse every day. Processing microblog text is difficult: the genre is noisy, documents have little context, and utterances are very short. As such, conventional NLP tools fail when faced with tweets and other microblog text. We present TwitIE, an open-source NLP pipeline customised to microblog text at every stage. Additionally, it includes Twitter-specific data import and metadata handling. This paper introduces each stage of the TwitIE pipeline, which is a modification of the GATE ANNIE open-source pipeline for news text. An evaluation against some state-of-the-art systems is also presented.

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  • Leon, in the paper you show ANNIE 60% on the dev set. The above 40% is on the entire ds that’s in svn. Feel free to replace that table, as you like. I could not load the dev set into GATE, due to its strange format. I am sure there’s a script somewhere that’ll convert it into a proper .conll format, I just had no time to find and run it. It’s ok, nobody will notice perhaps :)
  • These are mostly politicians. Often their names are preceded by their titles. There is also bigger context, within which entity coreference helps with detection (e.g. Atef and Mohammed Atef; bin Laden and Osama bin Laden).
  • These are names of friends, singers, artists, sportspeople, and celebrities. Often in lowercase, referred to by first or surname only and sometimes misspelled.
  • Hashtags: some contain locations, some – person names, and others are phrases For the @Mentions – IIRR Ritter (or some similar recent paper on Twitter NER) wrote that @mentions were excluded from their evaluation, since they are trivially recognisable as persons. Well, the point is – they are not all persons (used to be true). Now we have locations/facilities, organisations, as well as some products, research projects, and the like. Hence, even though it’s trivial to identify @mentions as an NE, assigning it the appropriate NE type is far from a solved problem!
  • Transcript of "TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text"

    1. 1. University of Sheffield, NLP TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text Kalina Bontcheva Leon Derczynski Adam Funk Mark A. Greenwood Diana Maynard Niraj Aswani © The University of Sheffield, 1995-2013 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs Licence
    2. 2. University of Sheffield, NLP The Problem • Running ANNIE on 300 news articles – 87% f-score • Running ANNIE on some tweets - < 40% f-score
    3. 3. University of Sheffield, NLP Example: Persons in news articles
    4. 4. University of Sheffield, NLP Example: Persons in tweets
    5. 5. University of Sheffield, NLP 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
    6. 6. University of Sheffield, NLP 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)
    7. 7. University of Sheffield, NLP TwitIE: GATE’s new Twitter NER pipeline
    8. 8. University of Sheffield, NLP 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
    9. 9. University of Sheffield, NLP 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
    10. 10. University of Sheffield, NLP Language Detection Examples
    11. 11. University of Sheffield, NLP 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].
    12. 12. University of Sheffield, NLP 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
    13. 13. University of Sheffield, NLP 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
    14. 14. University of Sheffield, NLP 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
    15. 15. University of Sheffield, NLP 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)
    16. 16. University of Sheffield, NLP 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
    17. 17. University of Sheffield, NLP 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
    18. 18. University of Sheffield, NLP TwitIE NER Results
    19. 19. University of Sheffield, NLP 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/
    20. 20. University of Sheffield, NLP Coming Soon: TwitIE-as-a-Service Preview of some text analytics services on AnnoMarket.com
    21. 21. 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!
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