ccg2lambda
Provide Semantic Parsing solutions and Natural Language Inferences for multiple languages following the idea of the syntax-semantics interface. (by mynlp)
nli4ct
By ai-systems
ccg2lambda | nli4ct | |
---|---|---|
1 | 1 | |
229 | 11 | |
0.4% | - | |
4.8 | 4.4 | |
5 months ago | 11 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | - |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ccg2lambda
Posts with mentions or reviews of ccg2lambda.
We have used some of these posts to build our list of alternatives
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nli4ct
Posts with mentions or reviews of nli4ct.
We have used some of these posts to build our list of alternatives
and similar projects.
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NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports
How can we interpret and retrieve medical evidence to support clinical decisions? Clinical trial reports (CTR) amassed over the years contain indispensable information for the development of personalized medicine. However, it is practically infeasible to manually inspect over 400,000+ clinical trial reports in order to find the best evidence for experimental treatments. Natural Language Inference (NLI) offers a potential solution to this problem, by allowing the scalable computation of textual entailment. However, existing NLI models perform poorly on biomedical corpora, and previously published datasets fail to capture the full complexity of inference over CTRs. In this work, we present a novel resource to advance research on NLI for reasoning on CTRs. The resource includes two main tasks. Firstly, to determine the inference relation between a natural language statement, and a CTR. Secondly, to retrieve supporting facts to justify the predicted relation. We provide NLI4CT, a corpus of 2400 statements and CTRs, annotated for these tasks. Baselines on this corpus expose the limitations of existing NLI models, with 6 state-of-the-art NLI models achieving a maximum F1 score of 0.627. To the best of our knowledge, we are the first to design a task that covers the interpretation of full CTRs. To encourage further work on this challenging dataset, we make the corpus, competition leaderboard, website and code to replicate the baseline experiments available at: https://github.com/ai-systems/nli4ct
What are some alternatives?
When comparing ccg2lambda and nli4ct you can also consider the following projects:
nlp-recipes - Natural Language Processing Best Practices & Examples
survey_kit - Flutter library to create beautiful surveys (aligned with ResearchKit on iOS)
opencog - A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)
TextFooler - A Model for Natural Language Attack on Text Classification and Inference
gluon-nlp - NLP made easy
SurveyKit - Android library to create beautiful surveys (aligned with ResearchKit on iOS)