20,156 research outputs found
Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification
We introduce globally normalized convolutional neural networks for joint
entity classification and relation extraction. In particular, we propose a way
to utilize a linear-chain conditional random field output layer for predicting
entity types and relations between entities at the same time. Our experiments
show that global normalization outperforms a locally normalized softmax layer
on a benchmark dataset.Comment: EMNLP 201
Globally Normalized Reader
Rapid progress has been made towards question answering (QA) systems that can
extract answers from text. Existing neural approaches make use of expensive
bi-directional attention mechanisms or score all possible answer spans,
limiting scalability. We propose instead to cast extractive QA as an iterative
search problem: select the answer's sentence, start word, and end word. This
representation reduces the space of each search step and allows computation to
be conditionally allocated to promising search paths. We show that globally
normalizing the decision process and back-propagating through beam search makes
this representation viable and learning efficient. We empirically demonstrate
the benefits of this approach using our model, Globally Normalized Reader
(GNR), which achieves the second highest single model performance on the
Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster
than bi-attention-flow. We also introduce a data-augmentation method to produce
semantically valid examples by aligning named entities to a knowledge base and
swapping them with new entities of the same type. This method improves the
performance of all models considered in this work and is of independent
interest for a variety of NLP tasks.Comment: Presented at EMNLP 201
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads
In processing human produced text using natural language processing (NLP)
techniques, two fundamental subtasks that arise are (i) segmentation of the
plain text into meaningful subunits (e.g., entities), and (ii) dependency
parsing, to establish relations between subunits. In this paper, we develop a
relatively simple and effective neural joint model that performs both
segmentation and dependency parsing together, instead of one after the other as
in most state-of-the-art works. We will focus in particular on the real estate
ad setting, aiming to convert an ad to a structured description, which we name
property tree, comprising the tasks of (1) identifying important entities of a
property (e.g., rooms) from classifieds and (2) structuring them into a tree
format. In this work, we propose a new joint model that is able to tackle the
two tasks simultaneously and construct the property tree by (i) avoiding the
error propagation that would arise from the subtasks one after the other in a
pipelined fashion, and (ii) exploiting the interactions between the subtasks.
For this purpose, we perform an extensive comparative study of the pipeline
methods and the new proposed joint model, reporting an improvement of over
three percentage points in the overall edge F1 score of the property tree.
Also, we propose attention methods, to encourage our model to focus on salient
tokens during the construction of the property tree. Thus we experimentally
demonstrate the usefulness of attentive neural architectures for the proposed
joint model, showcasing a further improvement of two percentage points in edge
F1 score for our application.Comment: Preprint - Accepted for publication in Expert Systems with
Application
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