Team No:48 developed an entity linking system for tweets that has three main components: mention detection, link generation, and entity disambiguation. For mention detection, they used classification and segmentation models trained on annotated tweets to extract entity mentions. For link generation, they generated relevant Wikipedia pages for each mention. For entity disambiguation, they ranked and selected the most relevant Wikipedia pages for each mention based on semantic similarity measures between entities. They manually annotated 100 tweets to evaluate their system and achieved an accuracy of around 52%.

1. Team No:48
Snigdha Agarwal
Arnav Sharma
Srimanikantha Tangudu
Anubhav Jaiswal
Entity Linking in Social Media

2. Introduction
Problem Statement: For a given tweet, find the entities and then using
contextual information about these entities, try to link it with the corresponding
information resources
● Microblogs capture an unprecedented amount of information
● Information extraction from microblog posts
● In our case, Twitter Entity Linking.

3. Related Works
● To Link or Not to Link? A Study on End-to-End Tweet
Entity Linking - Stephen Guo, Ming Wei Chang, Emre
Kiciman
● TAGME: On-the-fly Annotation of Short Text Fragments
(by Wikipedia Entities)

4. System Components
The project has been broken down into 3 major parts
● Mention Detection,
○ task of extraction of surface form candidates that can link to an entity
in the domain of interest
● Link Generation,
○ task of finding the relevant Wikipedia pages for each entity obtained in
the tweet
● Entity Disambiguation.
○ task of linking an extracted mention to a specific definition or instance
of an entity

5. Approach
Mention Detection
● Classification and Segmentation of named entities as
separate tasks
● Most words found in tweets are not part of an entity
● Annotated dataset to effectively learn a model of named
entities required

6. Approach
Mention Detection
● Segmentation
○ @usernames not considered as entity
- unambiguous
- trivial to identify with 100% accuracy
- would only serve to inflate performance statistics
○ Brown clusters and tagging system, chunking system and
capitalization system, have been used to generate features.

7. Approach
Mention Detection
● Classification
○ Tweets do not contain enough context
○ A large lists of entities and their types
○ Use of LabeledLDA
- Models each entity string as a mixture of types
- Information about an entity's distribution over types can be shared,
thus handling ambiguous entity strings
- For example, Amazon could correspond to a distribution over two
types:COMPANY, and LOCATION, whereas Apple might represent a
distribution over COMPANY, and FOOD.

8. Approach
Link Generation
● For each entity obtained in the previous step, find the
relevant Wikipedia pages
● Previously done using the Wikipedia library
● Results were given an inverted rank on the basis of
combination of Jaccard Similarity and commonness of
the entity.

9. Approach
Entity Disambiguation
● List of Wikipedia pages obtained in previous step
● Rank them according to the context of the tweet
● Pick out the most relevant ones

10. Approach
Entity Disambiguation
● Semantic similarity measure known as Relatedness used for
disambiguation
● Quantification of the relation between two Wikipedia entities
based upon their inlinks and outlinks.
- For one entity in tweet, the one with the highest rank in the
previous step is selected as the answer.
- For two entities, the pair giving the highest relatedness
measure is selected.
- For more than two entities, pair-wise relatedness is used.

11. Accuracy
● To calculate accuracy, we manually annotated around 100
tweets. While annotating, using human intelligence, we found
out the entities inside the tweet and linked it to it’s relevant
wikipedia page.
● The misses and hits were considered to calculate the
accuracy.
● The test set of 100 tweets was very diverse and contained
tweets which had multiple entities as well.
● The overall accuracy of the system was around ~52%
Results

12. Thank You