This presentation describes the participation of the UNIBA team in the Named Entity rEcognition and Linking (NEEL) Chal- lenge. We propose a knowledge-based algorithm able to recognize and link named entities in English tweets. The approach combines the simple Lesk algorithm with informa- tion coming from both a distributional semantic model and usage frequency of Wikipedia concepts. The algorithm per- forms poorly in the entity recognition, while it achieves good results in the disambiguation step.

1. UNIBA: Exploiting a
Distributional Semantic Model for
Disambiguating and
Linking Entities in Tweets
Pierpaolo Basile, Annalina Caputo, Giovanni
Semeraro, Fedelucio Narducci
{fedelucio.narducci, pierpaolo.basile}@uniba.it
#Microposts2015, NEEL Challenge, Florence 18th May 2015

2. The Challenge
Just watched Frozen for the first time ever and knew the
words to all the songs... How?! #productplacement
Problem: Find and link entities in tweets
ProductEntity type

3. Our Approach
• Entity Recognition
• using PoS-tag
• relying on n-grams
• Disambiguation
• knowledge-based method that combines a
Distributional Semantic Models (DSM) with prior
probability assigned to each DBpedia concept
• Type
• manual map for all types defined in the dbpedia-owl
ontology to the respective types in the task

4. Entity Recognition: Indexing
Frozen
<dbpedia.org/resource/Frozen_(Madonna_song)>
Frozen
<dbpedia.org/resource/Frozen_(2013_film)>
Apple
<dbpedia.org/resource/Apple_Inc.>
Apple Inc.
<dbpedia.org/resource/Apple_Inc.>
Barack Obama
<http://dbpedia.org/resource/Barack_Obama>
DBpedia titles file and DBpedia NLP resources
http://wifo5-04.informatik.uni-mannheim.de/downloads/datasets/
Indexing

5. Entity Recognition…
PoS-tagger
N-grams
generation
Tokenization and
Normalization
Candidate list
of surface
forms
Tweet

6. …Entity Recognition
Search and
Filtering
Search Score
Levenshtein Distance
Jaccard Index
Candidate list
of surface
forms
Candidate
entities and
list of possible
concepts

7. Disambiguation
Building the glosses
Building the context
Semantic Ranking
3-step approach

8. Disambiguation:
Building the glosses
"Frozen" is a song by American singer-
songwriter Madonna…
Frozen is a 2013 American 3D computer-
animated musical…
DBpedia extended abstracts

9. Disambiguation:
Building the context
Just watched Frozen for the first time ever and knew the
words to all the songs... How?! #productplacement
<just, watched, first, time, knew, words, all, songs,
how, product, placement>
Context

10. Disambiguation:
Semantic Ranking 1/3
• Words as points in a
mathematical space
• Close words are similar
• Word space is built analyzing
word co-occurrences in a
large corpus
• Vector composition using
superposition (+)

11. Disambiguation:
Semantic Ranking 2/3
word2vec: https://code.google.com/p/word2vec/
Distributional Semantic Model built on Wikipedia
Context
• Cosine similarity
between the gloss and
the context
• Linear combination
with a function which
takes into account the
usage of concepts in
Wikipedia

12. Disambiguation:
Semantic Ranking 3/3
Statistics about the usage of concepts in Wikipedia
𝑝 𝑐𝑖𝑗 𝑒𝑖 =
𝑡 𝑒𝑖, 𝑐𝑖𝑗 + 1
#𝑒𝑖 + |𝐶𝑖|
Concept probability
given the entity

13. 𝑝 𝑐𝑖𝑗 𝑒𝑖 =
𝑡 𝑒𝑖, 𝑐𝑖𝑗 + 1
#𝑒𝑖 + |𝐶𝑖|
Disambiguation: Semantic
overlap 3/3
Statistics about the usage of concepts in Wikipedia
Number of times
ei is linked as cij
Number of concepts
assigned to ei

14. Evaluation
• Development set
• 500 manually annotated tweets
• Metrics
• SLM: Strong Link Match
• STMM: Strong Typed Mention Match
• MC: Mention Ceaf
• System setup
• TweetNLP for tokenization and PoS-tagging
• word2vec for DSM building: 400 vector dimensions
analyzing only terms that occur at least 25 times
• Developed in JAVA

15. Results
• Low performance in entity recognition
• Good results in disambiguation: F=0.825
considering correct recognition and no-NIL
instances
Entity Recognition F-SLM F-STMM F-MC
PoS-tag 0.362 0.267 0.389
N-grams 0.258 0.191 0.306