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University of Sheffield, NLP TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text Kalina Bontcheva Leon Derczynski Adam Funk Mark.

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Presentation on theme: "University of Sheffield, NLP TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text Kalina Bontcheva Leon Derczynski Adam Funk Mark."— Presentation transcript:

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, This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs Licence

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 University of Sheffield, NLP Example: Persons in news articles

4 University of Sheffield, NLP Example: Persons in tweets

5 University of Sheffield, NLP Genre Differences in Entity Types NewsTweets PERPoliticians, business leaders, journalists, celebrities Sportsmen, actors, TV personalities, celebrities, names of friends LOCCountries, cities, rivers, and other places related to current affairs Restaurants, bars, local landmarks/areas, cities, rarely countries ORGPublic and private companies, government organisations Bands, internet companies, sports clubs

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, (ORG)

7 University of Sheffield, NLP TwitIE: GATE’s new Twitter NER pipeline

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 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 University of Sheffield, NLP Language Detection Examples

11 University of Sheffield, NLP Tokenisation Splitting a text into its constituent parts Plenty of “unusual”, but very important tokens in social media: – 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 University of Sheffield, NLP Example –#WiredBizCon #nike vp said saw what 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 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 addresses

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 -> 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 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 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 University of Sheffield, NLP Tweet Normalisation 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 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 University of Sheffield, NLP TwitIE NER Results

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.htmlhttps://gate.ac.uk/wiki/twitie.html Available soon as a web service on the forthcoming AnnoMarket NLP cloud marketplace: https://annomarket.com/

20 University of Sheffield, NLP Coming Soon: TwitIE-as-a-Service Preview of some text analytics services on AnnoMarket.com

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)http://www.trendminer-project.euhttp://www.ucomp.eu Thank you for your time!


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