Search Results for author:
Found 13 papers, 2 papers with code
Learning Interpretable Spatial Operations in a Rich 3D Blocks World
In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world.
Biomedical Event Extraction using Abstract Meaning Representation
We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text.
Unsupervised Neural Hidden Markov Models
In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model.
Extracting Structured Scholarly Information from the Machine Translation Literature
Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work.
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations.
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation
To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling.
Search-based Structured Prediction
We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision.
Statistical Phrase-Based Translation
We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models.
