85,294 research outputs found
Diffusion of Context and Credit Information in Markovian Models
This paper studies the problem of ergodicity of transition probability
matrices in Markovian models, such as hidden Markov models (HMMs), and how it
makes very difficult the task of learning to represent long-term context for
sequential data. This phenomenon hurts the forward propagation of long-term
context information, as well as learning a hidden state representation to
represent long-term context, which depends on propagating credit information
backwards in time. Using results from Markov chain theory, we show that this
problem of diffusion of context and credit is reduced when the transition
probabilities approach 0 or 1, i.e., the transition probability matrices are
sparse and the model essentially deterministic. The results found in this paper
apply to learning approaches based on continuous optimization, such as gradient
descent and the Baum-Welch algorithm.Comment: See http://www.jair.org/ for any accompanying file
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
- …