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•
•
•
Understanding of the
subject (human)
Extract features (human)
Search query
Predict search scores
Review a few results
(human)
Search results
•
•
Understanding of the
subject (human)
Parse and vectorize
documents
Predict scores
Review a few results
(human)
Document
labels Train supervised
machine learning model
•
•
•
•
•
•
•
•
•
•
•
•
•
•
github.com/FreeDiscovery/FreeDiscovery
Text vectorization
(BoW, n-grams)
Latent Semantic
Indexing (LSI/ LSA)
Raw documents
Logistic Regression,
SVM, xgboost, ..
K-Nearest
Neighbors
Birch + cluster
labeling
DBSCAN,
I-Match, simhash
JWZ algorithm
Sparse matrix
(10-100k dim)
Dense matrix
(100-300 dim)
FreeDiscovery core
Model / data
persistence
Document ID
mapping
REST API server
Nginx proxy
(optional)
• flask
• marshmallow webargs
• Werkzeug gunicorn
• flask-apispec
•
bootprint-openapi
Sphinx
•
•
•
•
•
•
•
•
•
MiniBatchKMeans
BIRCH DBSCAN
HDBSCAN,
¹ hdbscan.readthedocs.io/en/latest/performance_and_scalability.html
●
●
●
simhash-py
●
●
●
=>
Recall vs Documents Retrieved (Logistic Regression CV)
The average performance variation from baseline run with
Logistic Regression CV (BOW, log TF-IDF weight) for the ERDM
dataset (1000 train size, 700000 test size).
Reviewer
Document
labels
TAR system
Scores
predict
•
•
•
•
●
joblib
●
● pandas
● HashingVectorizer
Search query:
time grid search
Better search query?
scikit-learn project:
● 9100 issues / PR
● 850 open issues
● 540 open PR
● 90k comments
●
●
●
●
David Grossman,
Eugene Yang, Ophir Frieder
➔
➔
➔
➔
➔
➔ github.com/FreeDiscovery/FreeDiscovery
➔ freediscovery.io/doc/stable
@RomanYurchak

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