Giters
dbpedia
/
fact-extractor
Fact Extraction from Wikipedia Text
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Stargazers:
525
Watchers:
60
Issues:
66
Forks:
79
dbpedia/fact-extractor Issues
Add missing sentences to gold standard
Updated
4 years ago
Comments count
1
requirements.txt cannot be installed
Updated
6 years ago
Comments count
1
ImportError: No module named secrets
Updated
6 years ago
Comments count
1
produce_labeled_data.py appears to only use Italian stopwords
Updated
8 years ago
Comments count
2
Supervised frame confidence score is not taken into account
Updated
9 years ago
Cache entity linking results
Updated
9 years ago
No line feed added after feature label
Updated
9 years ago
Review code documentation
Closed
9 years ago
Comments count
6
Wrap up the project in a blog post
Closed
9 years ago
Publish Performance Measures Reports
Closed
9 years ago
Comments count
1
Refactor, clean and organize the code base
Closed
9 years ago
Comments count
3
Integrate CrowdFlower API for automatic job posting
Closed
9 years ago
Comments count
1
Complete supervised evaluation
Closed
9 years ago
Gold standard must comply to the latest sentence ID format
Closed
9 years ago
Comments count
1
Complete unsupervised evaluation
Closed
9 years ago
Makefile target for the unsupervised evaluation
Closed
9 years ago
Add a flag to toggle numerical FEs evaluation
Closed
9 years ago
Comments count
1
Automatically annotate numerical FEs
Closed
9 years ago
Comments count
4
Investigate supervised frame classification
Closed
9 years ago
Comments count
1
Exotic numerical FEs annotations in the unsupervised output
Closed
9 years ago
Keep track of the SVM probability score
Closed
9 years ago
Comments count
2
Mint a frame instance type assertion when a mapping to DBPO is found
Closed
9 years ago
Output datasets should comply to mappings
Closed
9 years ago
Integrate numerical FEs into the supervised classification
Closed
9 years ago
Map frames and FEs to DBPO properties
Closed
9 years ago
Comments count
1
Add a fact confidence score to the supervised results
Closed
9 years ago
Build gold standard for evaluation
Closed
9 years ago
Comments count
1
Integrate numerical FEs into gold standard
Closed
9 years ago
Serialize a dataset with triple confidence scores
Closed
9 years ago
Keep track of the linked entity confidence score at supervised runtime
Closed
9 years ago
Training sentences must be 1 sentence
Closed
9 years ago
Comments count
2
Add a fact confidence score to the unsupervised results
Closed
9 years ago
Use CrowdFlower aggregated results to produce the training data
Closed
9 years ago
Add a flexible command line to the supervised classifier run script
Closed
9 years ago
Comments count
1
Serialize supervised classification results into RDF
Closed
9 years ago
Comments count
1
Add entity linking results into the supervised classifier output
Closed
9 years ago
Add supervised classification as an internal module
Closed
9 years ago
Compute Fleiss' Kappa agreement score from the crowd results
Closed
9 years ago
When a duration FE is found, start and end date triples should be stated
Closed
9 years ago
Integrate numerical FEs into the unsupervised classification
Closed
9 years ago
Normalize Date Expressions before classification
Closed
9 years ago
Comments count
4
Normalize Date Expressions in Training Set
Closed
9 years ago
Comments count
22
The subject should be the article URI from which the fact was extracted
Closed
9 years ago
Comments count
2
Double-check validated gold standard (part 1)
Closed
9 years ago
Double-check validated gold standard (part 2)
Closed
9 years ago
Comments count
1
Validate crowdsourced gold standard (part 2)
Closed
9 years ago
Comments count
4
Validate crowdsourced gold standard (part 1)
Closed
9 years ago
Merge all the contiguous chunks into a big single one
Closed
9 years ago
Comments count
2
Prepare job data for the event
Closed
9 years ago
Comments count
4
Investigate how to port the date normalizer to Python
Closed
9 years ago
Comments count
1
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