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Found 11 papers, 1 papers with code
Odin's Runes: A Rule Language for Information Extraction
Odin is an information extraction framework that applies cascades of finite state automata over both surface text and syntactic dependency graphs.
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification
We propose a neural network architecture for QA that reranks answer justifications as an intermediate (and human-interpretable) step in answer selection.
Ranked #1 on
Question Answering
on AI2 Kaggle Dataset
Scientific Discovery as Link Prediction in Influence and Citation Graphs
We introduce a machine learning approach for the identification of {``}white spaces{''} in scientific knowledge.
Eidos, INDRA, \& Delphi: From Free Text to Executable Causal Models
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process.
Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text
Many of the most pressing current research problems (e. g., public health, food security, or climate change) require multi-disciplinary collaborations.
Odinson: A Fast Rule-based Information Extraction Framework
We present Odinson, a rule-based information extraction framework, which couples a simple yet powerful pattern language that can operate over multiple representations of text, with a runtime system that operates in near real time.
MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions
Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired.
Parsing as Tagging
For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left.
An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification
This paper explores an unsupervised approach to learning a compositional representation function for multi-word expressions (MWEs), and evaluates it on the Tratz dataset, which associates two-word expressions with the semantic relation between the compound constituents (e. g. the label employer is associated with the noun compound government agency) (Tratz, 2011).
