Named entity recognition and disambiguation are important for information extraction and populating knowledge bases. Detecting and classifying named entities has traditionally been taken on by the natural language processing community, whilst linking of entities to external resources, such as DBpedia and GeoNames, has been the domain of the Semantic Web community. As these tasks are treated in different communities, it is difficult to assess the performance of these tasks combined. We present results on an evaluation of the NERD-ML approach on newswire and tweets for both Named Entity Recognition and Named Entity Disambiguation. Presented at CLIN 24: http://clin24.inl.nl/ http://nerd.eurecom.fr https://github.com/giusepperizzo/nerdml

1. EVALUATING NAMED ENTITY
RECOGNITION AND DISAMBIGUATION
IN NEWS AND TWEETS
Giuseppe Rizzo
Università degli studi di Torino
Marieke van Erp
VU University Amsterdam
Raphaël Troncy
EURECOM

2. EVALUATING NER & NED
•
NER typically an NLP task (MUC, CoNLL, ACE)
•
NED took flight with availability of large structured
resources (Wikipedia, DBpedia, Freebase)
•
Tools for NER and NED have started popping up outside
regular research outlets (TextRazor, DBpedia Spotlight,
AlchemyAPI)
•
Unclear how well these tools perform

3. THIS WORK
•
Evaluation & comparison of 10 out-of-the-box
NER and NED tools through NERD API as well as
a combination of the tools in NERD-ML
•
Two types of data: Newswire & Tweets

4. •
http://nerd.eurecom.fr
•
Ontology, REST API & UI
•
Uniform access to 12 different extractors/linkers:
AlchemyAPI, DBpedia Spotlight, Extractiv, Lupedia,
OpenCalais, Saplo, SemiTags, TextRazor, THD, Wikimeta,
Yahoo! Content Analysis, Zemanta

5. NERD-ML
•
The aim of NERD-ML is to combine the
knowledge of the different extractors into a better
named entity recogniser
•
Uses NERD predictions, Stanford NER & extra
features
•
Naive Bayes, k-NN, SMO

6. DATA
•
CoNLL 2003 English NER with AIDA CoNLL-YAGO
links to Wikipedia (5,648 NEs/4,485 links in test set)
•
Making Sense of Microposts 2013 (MSM’13) for
NER on Twitter domain + 62 randomly selected
tweets from Ritter et al.’s corpus with links to
DBpedia resources (MSM: 1,538 NEs/Ritter: 177
links in test set)

7. PER
LOC
ORG
Precision
Precision
MISC
OVERALL
100
90
80
70
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
90
100
RESULTS NER NEWSWIRE
PER
LOC
ORG
Recall
Recall
MISC
OVERALL
PER
LOC
ORG
MISC
OVERALL
F1
F1
AlchemyAPI
DBpedia Spotlight
Extractiv
Lupedia
OpenCalais
Saplo
Textrazor
Yahoo
Wikimeta
Zemanta
Stanford NER
NERD-ML Run01
NERD-ML Run02
NERD-ML Run03
Upper Limit

8. PER
LOC
ORG
Precision
Precision
MISC
OVERALL
100
0
10
20
30
40
50
60
70
80
90
100
90
80
70
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
90
100
RESULTS NER MSM
PER
LOC
ORG
Precision
Recall
MISC
OVERALL
PER
LOC
ORG
MISC
OVERALL
Precision
F1
AlchemyAPI
DBpedia Spotlight
Extractiv
Lupedia
OpenCalais
Saplo
Textrazor
Wikimeta
Zemanta
Ritter et al.
Stanford NER
NERD-ML Run01
NERD-ML Run02
NERD-ML Run03
Upper Limit

9. RESULTS NED
DBpedia
AlchemyAPI
Extractiv Lupedia Textrazor Yahoo Zemanta
Spotlight
AIDAYAGO
70.63
26.93
51.31
57.98
49.21
0.0
35.58
TWEETS
53.85
25.13
74.07
65.38
58.14
76.00
48.57

10. DISCUSSION
•
Still a ways to go, but for certain classes NER is
getting close to really good results
•
MISC class is (and probably always will be?) hard
•
Bigger datasets needed (for tweets and NED)
•
NED task can use standardisation

11. THANK YOU FOR LISTENING
•
Try out our code at:
https://github.com/giusepperizzo/nerdml

12. ACKNOWLEDGEMENTS
This research is funded through the LinkedTV
and NewsReader projects, both funded by the
European Union’s 7th Framework Programme
grants GA 287911 and ICT-316404).