Case Study | How LinkedIn used NLP to design Help Search System
This is the summary and key takeaways from the original post by LinkedIn on how NLP is being used (as of 2019) in designing its Help Search System.
Problem Statement:
Given a query by a user, fetch the most relevant Help Article from the database.
Iteration 1: Initial Solution
- Indexed all the help articles (documents) in the database using Lucene Index. In short, it generates an inverted dictionary that maps terms to all the documents it appeared in.
2. The given query is used to fetch all the relevant documents (hits) using Lucene indexing.
3. Each hit is scored using the BM25F algorithm, which takes the document structure into account, giving the highest weights to hits in the Title, then hits in the Keywords and then the Body, and returns a weighted score.
4. Return the best-scored articles.
Why it failed
Since the document retrieval system is Term based (syntactics), without taking semantics into account, the following are two example failure cases:
Iteration 2: Final Solution
Step 1: Text Normalization
“how canceling my premium accounts immediately” normalized to “cancel premium account”
Step 2: Query Mapping
It might happen that the normalized query doesn’t have any words in common with the words in the articles. Hence, each query is mapped to a more representative query to fill the gap between a user’s terminology and the article’s terminology.
Done in the following two steps:
- Query Grouping: Queries are grouped together based on similarity metrics
2. Topic Mining and Rep Scoring: For each of the queries in the Query group, a repScore is calculated and the top K queries are selected as Rep Queries
sim(RQ, Q2) is the similarity between the raw query and another query in the group
sim(Q2, title) is the maximum similarity between Q2 and one of the topics from the title (similarly for the body)
Step 3: Intent Classification
Long-tailed queries might not have a Rep Query, in which case a CNN is used for classifying the Intent of the query.
For example: “Canceling Your Premium Subscription” and “Canceling or Updating a Premium Subscription Purchased on Your Apple Device” are considered to have the same intent of “cancel premium.”
Overall Flow
