Search Results for author:
Found 17 papers, 4 papers with code
This before That: Causal Precedence in the Biomedical Domain
Causal precedence between biochemical interactions is crucial in the biomedical domain, because it transforms collections of individual interactions, e. g., bindings and phosphorylations, into the causal mechanisms needed to inform meaningful search and inference.
Text Annotation Graphs: Annotating Complex Natural Language Phenomena
Additionally, we include an approach to representing text annotations in which annotation subgraphs, or semantic summaries, are used to show relationships outside of the sequential context of the text itself.
Description of the Odin Event Extraction Framework and Rule Language
Here we include a thorough definition of the Odin rule language, together with a description of the Odin API in the Scala language, which allows one to apply these rules to arbitrary texts.
Sieve-based Coreference Resolution in the Biomedical Domain
We describe challenges and advantages unique to coreference resolution in the biomedical domain, and a sieve-based architecture that leverages domain knowledge for both entity and event coreference resolution.
SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction
We propose an approach for biomedical information extraction that marries the advantages of machine learning models, e. g., learning directly from data, with the benefits of rule-based approaches, e. g., interpretability.
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.
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.
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.
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.
Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder
Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier.
Computer-assisted construct classification of organizational performance concerning different stakeholder groups
The number of research articles in business and management has dramatically increased along with terminology, constructs, and measures.
Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features
This work investigates the value of augmenting recurrent neural networks with feature engineering for the Second Nuanced Arabic Dialect Identification (NADI) Subtask 1. 2: Country-level DA identification.
A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction
We propose a system that assists a user in constructing transparent information extraction models, consisting of patterns (or rules) written in a declarative language, through program synthesis. Users of our system can specify their requirements through the use of examples, which are collected with a search interface. The rule-synthesis system proposes rule candidates and the results of applying them on a textual corpus; the user has the option to accept the candidate, request another option, or adjust the examples provided to the system. Through an interactive evaluation, we show that our approach generates high-precision rules even in a 1-shot setting.
Syntax-driven Data Augmentation for Named Entity Recognition
In low resource settings, data augmentation strategies are commonly leveraged to improve performance.
Neural-Guided Program Synthesis of Information Extraction Rules Using Self-Supervision
We propose a neural-based approach for rule synthesis designed to help bridge the gap between the interpretability, precision and maintainability exhibited by rule-based information extraction systems with the scalability and convenience of statistical information extraction systems.
