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Named Entity Analysis and Representation
Faculty: Vasudeva Varma
TA: Aishvarya V Singh
Team: Satya Madala
Sakhineti Venkat
Sumol Lodha
Introduction
● The approach in entity specific mining is to make use of different APIs for
information retrieval based on the entity type.
● Analyze this information fetched from multiple sources and represent it in a
summarized form
Motivation
● A lot of entity specific information is present on the internet sparsely
located, seldom used and unaccounted for.
● This project aims to get the pieces of information together to give a
collective feedback about an entity.
Entity
● We narrow down our scope to
○ Place Entity
○ People Entity
Approach
● Identifying the entities and limiting the domain.
● Identifying different API’s for each named entity.
● Collecting data from different APIs for a single entity.
● Merging the extracted information.
Architecture
Challenges
● Redundancy removal between information streams from different APIs.
● Extracting latest information (eg. based on time-stamp).
● Getting the relevant response from various APIs in a very short time.
● Identifying authenticity of information Ensuring.
● Ensuring consistency of data across all streams.
Results
Future Work
● Automating the process of entity classification
● Extending the number of entity types to include entities like products, job
postings,
● Building recommendation engines based on our analysis for entities like
movies, songs.. etc.
● Providing multilingual and cross-lingual support.
● Spelling corrections for inputs
THANK YOU

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Named Entity Analysis and Representation

  • 1. Named Entity Analysis and Representation Faculty: Vasudeva Varma TA: Aishvarya V Singh Team: Satya Madala Sakhineti Venkat Sumol Lodha
  • 2. Introduction ● The approach in entity specific mining is to make use of different APIs for information retrieval based on the entity type. ● Analyze this information fetched from multiple sources and represent it in a summarized form
  • 3. Motivation ● A lot of entity specific information is present on the internet sparsely located, seldom used and unaccounted for. ● This project aims to get the pieces of information together to give a collective feedback about an entity.
  • 4. Entity ● We narrow down our scope to ○ Place Entity ○ People Entity
  • 5. Approach ● Identifying the entities and limiting the domain. ● Identifying different API’s for each named entity. ● Collecting data from different APIs for a single entity. ● Merging the extracted information.
  • 7. Challenges ● Redundancy removal between information streams from different APIs. ● Extracting latest information (eg. based on time-stamp). ● Getting the relevant response from various APIs in a very short time. ● Identifying authenticity of information Ensuring. ● Ensuring consistency of data across all streams.
  • 9. Future Work ● Automating the process of entity classification ● Extending the number of entity types to include entities like products, job postings, ● Building recommendation engines based on our analysis for entities like movies, songs.. etc. ● Providing multilingual and cross-lingual support. ● Spelling corrections for inputs