Information Extraction
2 papers with code • 0 benchmarks • 0 datasets
Information extraction is the task of automatically extracting structured information from unstructured and / or semi-structured machine-readable documents and other electronically represented sources (Source: Wikipedia).
Benchmarks
These leaderboards are used to track progress in Information Extraction
Subtasks
- Event Extraction
- Extractive Summarization
- Joint Entity and Relation Extraction
- Temporal Information Extraction
- Temporal Information Extraction
- Low Resource Named Entity Recognition
- Document-level Event Extraction
- Attribute Value Extraction
- Drug–drug Interaction Extraction
- Event Relation Extraction
- Definition Extraction
- Information Threading
- Participant Intervention Comparison Outcome Extraction
- Catalog Extraction
Latest papers
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i. e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost.
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances
This paper describes DUALIST, an active learning annotation paradigm which solicits and learns from labels on both features (e. g., words) and instances (e. g., documents).