1,661 research outputs found
Arc-swift: A Novel Transition System for Dependency Parsing
Transition-based dependency parsers often need sequences of local shift and
reduce operations to produce certain attachments. Correct individual decisions
hence require global information about the sentence context and mistakes cause
error propagation. This paper proposes a novel transition system, arc-swift,
that enables direct attachments between tokens farther apart with a single
transition. This allows the parser to leverage lexical information more
directly in transition decisions. Hence, arc-swift can achieve significantly
better performance with a very small beam size. Our parsers reduce error by
3.7--7.6% relative to those using existing transition systems on the Penn
Treebank dependency parsing task and English Universal Dependencies.Comment: Accepted at ACL 201
Learning Dynamic Feature Selection for Fast Sequential Prediction
We present paired learning and inference algorithms for significantly
reducing computation and increasing speed of the vector dot products in the
classifiers that are at the heart of many NLP components. This is accomplished
by partitioning the features into a sequence of templates which are ordered
such that high confidence can often be reached using only a small fraction of
all features. Parameter estimation is arranged to maximize accuracy and early
confidence in this sequence. Our approach is simpler and better suited to NLP
than other related cascade methods. We present experiments in left-to-right
part-of-speech tagging, named entity recognition, and transition-based
dependency parsing. On the typical benchmarking datasets we can preserve POS
tagging accuracy above 97% and parsing LAS above 88.5% both with over a
five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase
in speed.Comment: Appears in The 53rd Annual Meeting of the Association for
Computational Linguistics, Beijing, China, July 201
Parallel Natural Language Parsing: From Analysis to Speedup
Electrical Engineering, Mathematics and Computer Scienc
Reproducibility and Generalization of a Relation Extraction System for Gene-Disease Associations
Biomedical literature is a rich source of information on Gene-Disease Associations
(GDAs) that could help physicians in assessing clinical decisions and improve patient
care. GDAs are publicly available in databases containing relationships between
gene/miRNA expression and related diseases such as specific types of cancer.
Most of these resources, such as DisGeNET, miR2Disease and BioXpress, include
also manually curated data from publications. Human annotations are expensive
and cannot scale to the huge amount of data available in scientific literature (e.g.,
biomedical abstracts). Therefore, developing automated tools to identify GDAs is
getting traction in the community. Such systems employ Relation Extraction (RE)
techniques to extract information on gene/microRNA expression in diseases from
text. Once an automated text-mining tool has been developed, it can be tested on
human annotated data or it can be compared to state-of-the-art systems.
In this work we reproduce DEXTER, a system to automatically extract Gene-
Disease Associations (GDAs) from biomedical abstracts. The goal is to provide a
benchmark for future works regarding Relation Extraction (RE), enabling researchers
to test and compare their results.
The implemented version of DEXTER is available in the following git repository:
https://github.com/mntlra/DEXTER.Biomedical literature is a rich source of information on Gene-Disease Associations
(GDAs) that could help physicians in assessing clinical decisions and improve patient
care. GDAs are publicly available in databases containing relationships between
gene/miRNA expression and related diseases such as specific types of cancer.
Most of these resources, such as DisGeNET, miR2Disease and BioXpress, include
also manually curated data from publications. Human annotations are expensive
and cannot scale to the huge amount of data available in scientific literature (e.g.,
biomedical abstracts). Therefore, developing automated tools to identify GDAs is
getting traction in the community. Such systems employ Relation Extraction (RE)
techniques to extract information on gene/microRNA expression in diseases from
text. Once an automated text-mining tool has been developed, it can be tested on
human annotated data or it can be compared to state-of-the-art systems.
In this work we reproduce DEXTER, a system to automatically extract Gene-
Disease Associations (GDAs) from biomedical abstracts. The goal is to provide a
benchmark for future works regarding Relation Extraction (RE), enabling researchers
to test and compare their results.
The implemented version of DEXTER is available in the following git repository:
https://github.com/mntlra/DEXTER
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Machine Learning Models for Efficient and Robust Natural Language Processing
Natural language processing (NLP) has come of age. For example, semantic role labeling (SRL), which automatically annotates sentences with a labeled graph representing who did what to whom, has in the past ten years seen nearly 40% reduction in error, bringing it to useful accuracy. As a result, a myriad of practitioners now want to deploy NLP systems on billions of documents across many domains. However, state-of-the-art NLP systems are typically not optimized for cross-domain robustness nor computational efficiency. In this dissertation I develop machine learning methods to facilitate fast and robust inference across many common NLP tasks.
First, I describe paired learning and inference algorithms for dynamic feature selection which accelerate inference in linear classifiers, the heart of the fastest NLP models, by 5-10 times. I then present iterated dilated convolutional neural networks (ID-CNNs), a distinct combination of network structure, parameter sharing and training procedures that increase inference speed by 14-20 times with accuracy matching bidirectional LSTMs, the most accurate models for NLP sequence labeling. Finally, I describe linguistically-informed self-attention (LISA), a neural network model that combines multi-head self-attention with multi-task learning to facilitate improved generalization to new domains. We show that incorporating linguistic structure in this way leads to substantial improvements over the previous state-of-the-art (syntax-free) neural network models for SRL, especially when evaluating out-of-domain. I conclude with a brief discussion of potential future directions stemming from my thesis work
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