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1,700 | An Autoencoder Approach to Learning Bilingual Word Representations | cs.CL | Cross-language learning allows us to use training data from one language to
build models for a different language. Many approaches to bilingual learning
require that we have word-level alignment of sentences from parallel corpora.
In this work we explore the use of autoencoder-based methods for cross-language
learning ... | computer science |
1,701 | word2vec Explained: deriving Mikolov et al.'s negative-sampling
word-embedding method | cs.CL | The word2vec software of Tomas Mikolov and colleagues
(https://code.google.com/p/word2vec/ ) has gained a lot of traction lately, and
provides state-of-the-art word embeddings. The learning models behind the
software are described in two research papers. We found the description of the
models in these papers to be some... | computer science |
1,702 | Modelling Data Dispersion Degree in Automatic Robust Estimation for
Multivariate Gaussian Mixture Models with an Application to Noisy Speech
Processing | cs.CL | The trimming scheme with a prefixed cutoff portion is known as a method of
improving the robustness of statistical models such as multivariate Gaussian
mixture models (MG- MMs) in small scale tests by alleviating the impacts of
outliers. However, when this method is applied to real- world data, such as
noisy speech pro... | computer science |
1,703 | A Case Study in Text Mining: Interpreting Twitter Data From World Cup
Tweets | stat.ML | Cluster analysis is a field of data analysis that extracts underlying
patterns in data. One application of cluster analysis is in text-mining, the
analysis of large collections of text to find similarities between documents.
We used a collection of about 30,000 tweets extracted from Twitter just before
the World Cup st... | computer science |
1,704 | Use of Modality and Negation in Semantically-Informed Syntactic MT | cs.CL | This paper describes the resource- and system-building efforts of an
eight-week Johns Hopkins University Human Language Technology Center of
Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on
Semantically-Informed Machine Translation (SIMT). We describe a new
modality/negation (MN) annotation schem... | computer science |
1,705 | RAND-WALK: A Latent Variable Model Approach to Word Embeddings | cs.LG | Semantic word embeddings represent the meaning of a word via a vector, and
are created by diverse methods. Many use nonlinear operations on co-occurrence
statistics, and have hand-tuned hyperparameters and reweighting methods.
This paper proposes a new generative model, a dynamic version of the
log-linear topic model... | computer science |
1,706 | A Linear Dynamical System Model for Text | stat.ML | Low dimensional representations of words allow accurate NLP models to be
trained on limited annotated data. While most representations ignore words'
local context, a natural way to induce context-dependent representations is to
perform inference in a probabilistic latent-variable sequence model. Given the
recent succes... | computer science |
1,707 | Using NLP to measure democracy | cs.CL | This paper uses natural language processing to create the first machine-coded
democracy index, which I call Automated Democracy Scores (ADS). The ADS are
based on 42 million news articles from 6,043 different sources and cover all
independent countries in the 1993-2012 period. Unlike the democracy indices we
have today... | computer science |
1,708 | Bayesian Optimization of Text Representations | cs.CL | When applying machine learning to problems in NLP, there are many choices to
make about how to represent input texts. These choices can have a big effect on
performance, but they are often uninteresting to researchers or practitioners
who simply need a module that performs well. We propose an approach to
optimizing ove... | computer science |
1,709 | Statistical modality tagging from rule-based annotations and
crowdsourcing | cs.CL | We explore training an automatic modality tagger. Modality is the attitude
that a speaker might have toward an event or state. One of the main hurdles for
training a linguistic tagger is gathering training data. This is particularly
problematic for training a tagger for modality because modality triggers are
sparse for... | computer science |
1,710 | Bethe Projections for Non-Local Inference | stat.ML | Many inference problems in structured prediction are naturally solved by
augmenting a tractable dependency structure with complex, non-local auxiliary
objectives. This includes the mean field family of variational inference
algorithms, soft- or hard-constrained inference using Lagrangian relaxation or
linear programmin... | computer science |
1,711 | Nonparametric Relational Topic Models through Dependent Gamma Processes | stat.ML | Traditional Relational Topic Models provide a way to discover the hidden
topics from a document network. Many theoretical and practical tasks, such as
dimensional reduction, document clustering, link prediction, benefit from this
revealed knowledge. However, existing relational topic models are based on an
assumption t... | computer science |
1,712 | Active Learning for Speech Recognition: the Power of Gradients | cs.CL | In training speech recognition systems, labeling audio clips can be
expensive, and not all data is equally valuable. Active learning aims to label
only the most informative samples to reduce cost. For speech recognition,
confidence scores and other likelihood-based active learning methods have been
shown to be effectiv... | computer science |
1,713 | A Simple Approach to Multilingual Polarity Classification in Twitter | cs.CL | Recently, sentiment analysis has received a lot of attention due to the
interest in mining opinions of social media users. Sentiment analysis consists
in determining the polarity of a given text, i.e., its degree of positiveness
or negativeness. Traditionally, Sentiment Analysis algorithms have been
tailored to a speci... | computer science |
1,714 | "What is Relevant in a Text Document?": An Interpretable Machine
Learning Approach | cs.CL | Text documents can be described by a number of abstract concepts such as
semantic category, writing style, or sentiment. Machine learning (ML) models
have been trained to automatically map documents to these abstract concepts,
allowing to annotate very large text collections, more than could be processed
by a human in ... | computer science |
1,715 | Improved Bayesian Logistic Supervised Topic Models with Data
Augmentation | cs.LG | Supervised topic models with a logistic likelihood have two issues that
potentially limit their practical use: 1) response variables are usually
over-weighted by document word counts; and 2) existing variational inference
methods make strict mean-field assumptions. We address these issues by: 1)
introducing a regulariz... | computer science |
1,716 | A Bayesian Network View on Acoustic Model-Based Techniques for Robust
Speech Recognition | cs.LG | This article provides a unifying Bayesian network view on various approaches
for acoustic model adaptation, missing feature, and uncertainty decoding that
are well-known in the literature of robust automatic speech recognition. The
representatives of these classes can often be deduced from a Bayesian network
that exten... | computer science |
1,717 | Distributed Representations of Words and Phrases and their
Compositionality | cs.CL | The recently introduced continuous Skip-gram model is an efficient method for
learning high-quality distributed vector representations that capture a large
number of precise syntactic and semantic word relationships. In this paper we
present several extensions that improve both the quality of the vectors and the
traini... | computer science |
1,718 | Bidirectional Recursive Neural Networks for Token-Level Labeling with
Structure | cs.LG | Recently, deep architectures, such as recurrent and recursive neural networks
have been successfully applied to various natural language processing tasks.
Inspired by bidirectional recurrent neural networks which use representations
that summarize the past and future around an instance, we propose a novel
architecture ... | computer science |
1,719 | Language Modeling with Power Low Rank Ensembles | cs.CL | We present power low rank ensembles (PLRE), a flexible framework for n-gram
language modeling where ensembles of low rank matrices and tensors are used to
obtain smoothed probability estimates of words in context. Our method can be
understood as a generalization of n-gram modeling to non-integer n, and
includes standar... | computer science |
1,720 | An Approach to Reducing Annotation Costs for BioNLP | cs.CL | There is a broad range of BioNLP tasks for which active learning (AL) can
significantly reduce annotation costs and a specific AL algorithm we have
developed is particularly effective in reducing annotation costs for these
tasks. We have previously developed an AL algorithm called ClosestInitPA that
works best with tas... | computer science |
1,721 | Taking into Account the Differences between Actively and Passively
Acquired Data: The Case of Active Learning with Support Vector Machines for
Imbalanced Datasets | cs.LG | Actively sampled data can have very different characteristics than passively
sampled data. Therefore, it's promising to investigate using different
inference procedures during AL than are used during passive learning (PL). This
general idea is explored in detail for the focused case of AL with
cost-weighted SVMs for im... | computer science |
1,722 | A Method for Stopping Active Learning Based on Stabilizing Predictions
and the Need for User-Adjustable Stopping | cs.LG | A survey of existing methods for stopping active learning (AL) reveals the
needs for methods that are: more widely applicable; more aggressive in saving
annotations; and more stable across changing datasets. A new method for
stopping AL based on stabilizing predictions is presented that addresses these
needs. Furthermo... | computer science |
1,723 | Semantically-Informed Syntactic Machine Translation: A Tree-Grafting
Approach | cs.CL | We describe a unified and coherent syntactic framework for supporting a
semantically-informed syntactic approach to statistical machine translation.
Semantically enriched syntactic tags assigned to the target-language training
texts improved translation quality. The resulting system significantly
outperformed a linguis... | computer science |
1,724 | BilBOWA: Fast Bilingual Distributed Representations without Word
Alignments | stat.ML | We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple
and computationally-efficient model for learning bilingual distributed
representations of words which can scale to large monolingual datasets and does
not require word-aligned parallel training data. Instead it trains directly on
monolingual dat... | computer science |
1,725 | Graph-Sparse LDA: A Topic Model with Structured Sparsity | stat.ML | Originally designed to model text, topic modeling has become a powerful tool
for uncovering latent structure in domains including medicine, finance, and
vision. The goals for the model vary depending on the application: in some
cases, the discovered topics may be used for prediction or some other
downstream task. In ot... | computer science |
1,726 | Using Mechanical Turk to Build Machine Translation Evaluation Sets | cs.CL | Building machine translation (MT) test sets is a relatively expensive task.
As MT becomes increasingly desired for more and more language pairs and more
and more domains, it becomes necessary to build test sets for each case. In
this paper, we investigate using Amazon's Mechanical Turk (MTurk) to make MT
test sets chea... | computer science |
1,727 | Bucking the Trend: Large-Scale Cost-Focused Active Learning for
Statistical Machine Translation | cs.CL | We explore how to improve machine translation systems by adding more
translation data in situations where we already have substantial resources. The
main challenge is how to buck the trend of diminishing returns that is commonly
encountered. We present an active learning-style data solicitation algorithm to
meet this c... | computer science |
1,728 | A random forest system combination approach for error detection in
digital dictionaries | cs.CL | When digitizing a print bilingual dictionary, whether via optical character
recognition or manual entry, it is inevitable that errors are introduced into
the electronic version that is created. We investigate automating the process
of detecting errors in an XML representation of a digitized print dictionary
using a hyb... | computer science |
1,729 | Rapid Adaptation of POS Tagging for Domain Specific Uses | cs.CL | Part-of-speech (POS) tagging is a fundamental component for performing
natural language tasks such as parsing, information extraction, and question
answering. When POS taggers are trained in one domain and applied in
significantly different domains, their performance can degrade dramatically. We
present a methodology f... | computer science |
1,730 | The Bayesian Echo Chamber: Modeling Social Influence via Linguistic
Accommodation | stat.ML | We present the Bayesian Echo Chamber, a new Bayesian generative model for
social interaction data. By modeling the evolution of people's language usage
over time, this model discovers latent influence relationships between them.
Unlike previous work on inferring influence, which has primarily focused on
simple temporal... | computer science |
1,731 | Learning Multi-Relational Semantics Using Neural-Embedding Models | cs.CL | In this paper we present a unified framework for modeling multi-relational
representations, scoring, and learning, and conduct an empirical study of
several recent multi-relational embedding models under the framework. We
investigate the different choices of relation operators based on linear and
bilinear transformatio... | computer science |
1,732 | Linking GloVe with word2vec | cs.CL | The Global Vectors for word representation (GloVe), introduced by Jeffrey
Pennington et al. is reported to be an efficient and effective method for
learning vector representations of words. State-of-the-art performance is also
provided by skip-gram with negative-sampling (SGNS) implemented in the word2vec
tool. In this... | computer science |
1,733 | A Joint Probabilistic Classification Model of Relevant and Irrelevant
Sentences in Mathematical Word Problems | cs.CL | Estimating the difficulty level of math word problems is an important task
for many educational applications. Identification of relevant and irrelevant
sentences in math word problems is an important step for calculating the
difficulty levels of such problems. This paper addresses a novel application of
text categoriza... | computer science |
1,734 | Effective Use of Word Order for Text Categorization with Convolutional
Neural Networks | cs.CL | Convolutional neural network (CNN) is a neural network that can make use of
the internal structure of data such as the 2D structure of image data. This
paper studies CNN on text categorization to exploit the 1D structure (namely,
word order) of text data for accurate prediction. Instead of using
low-dimensional word ve... | computer science |
1,735 | Inducing Semantic Representation from Text by Jointly Predicting and
Factorizing Relations | cs.CL | In this work, we propose a new method to integrate two recent lines of work:
unsupervised induction of shallow semantics (e.g., semantic roles) and
factorization of relations in text and knowledge bases. Our model consists of
two components: (1) an encoding component: a semantic role labeling model which
predicts roles... | computer science |
1,736 | Modeling Compositionality with Multiplicative Recurrent Neural Networks | cs.LG | We present the multiplicative recurrent neural network as a general model for
compositional meaning in language, and evaluate it on the task of fine-grained
sentiment analysis. We establish a connection to the previously investigated
matrix-space models for compositionality, and show they are special cases of
the multi... | computer science |
1,737 | On Learning Vector Representations in Hierarchical Label Spaces | cs.LG | An important problem in multi-label classification is to capture label
patterns or underlying structures that have an impact on such patterns. This
paper addresses one such problem, namely how to exploit hierarchical structures
over labels. We present a novel method to learn vector representations of a
label space give... | computer science |
1,738 | Grammar as a Foreign Language | cs.CL | Syntactic constituency parsing is a fundamental problem in natural language
processing and has been the subject of intensive research and engineering for
decades. As a result, the most accurate parsers are domain specific, complex,
and inefficient. In this paper we show that the domain agnostic
attention-enhanced seque... | computer science |
1,739 | Deep Belief Nets for Topic Modeling | cs.CL | Applying traditional collaborative filtering to digital publishing is
challenging because user data is very sparse due to the high volume of
documents relative to the number of users. Content based approaches, on the
other hand, is attractive because textual content is often very informative. In
this paper we describe ... | computer science |
1,740 | Semi-supervised Convolutional Neural Networks for Text Categorization
via Region Embedding | stat.ML | This paper presents a new semi-supervised framework with convolutional neural
networks (CNNs) for text categorization. Unlike the previous approaches that
rely on word embeddings, our method learns embeddings of small text regions
from unlabeled data for integration into a supervised CNN. The proposed scheme
for embedd... | computer science |
1,741 | Analysis of Stopping Active Learning based on Stabilizing Predictions | cs.LG | Within the natural language processing (NLP) community, active learning has
been widely investigated and applied in order to alleviate the annotation
bottleneck faced by developers of new NLP systems and technologies. This paper
presents the first theoretical analysis of stopping active learning based on
stabilizing pr... | computer science |
1,742 | WordRank: Learning Word Embeddings via Robust Ranking | cs.CL | Embedding words in a vector space has gained a lot of attention in recent
years. While state-of-the-art methods provide efficient computation of word
similarities via a low-dimensional matrix embedding, their motivation is often
left unclear. In this paper, we argue that word embedding can be naturally
viewed as a rank... | computer science |
1,743 | On the accuracy of self-normalized log-linear models | stat.ML | Calculation of the log-normalizer is a major computational obstacle in
applications of log-linear models with large output spaces. The problem of fast
normalizer computation has therefore attracted significant attention in the
theoretical and applied machine learning literature. In this paper, we analyze
a recently pro... | computer science |
1,744 | Efficient Learning for Undirected Topic Models | cs.LG | Replicated Softmax model, a well-known undirected topic model, is powerful in
extracting semantic representations of documents. Traditional learning
strategies such as Contrastive Divergence are very inefficient. This paper
provides a novel estimator to speed up the learning based on Noise Contrastive
Estimate, extende... | computer science |
1,745 | Clustering is Efficient for Approximate Maximum Inner Product Search | cs.LG | Efficient Maximum Inner Product Search (MIPS) is an important task that has a
wide applicability in recommendation systems and classification with a large
number of classes. Solutions based on locality-sensitive hashing (LSH) as well
as tree-based solutions have been investigated in the recent literature, to
perform ap... | computer science |
1,746 | Tag-Weighted Topic Model For Large-scale Semi-Structured Documents | cs.CL | To date, there have been massive Semi-Structured Documents (SSDs) during the
evolution of the Internet. These SSDs contain both unstructured features (e.g.,
plain text) and metadata (e.g., tags). Most previous works focused on modeling
the unstructured text, and recently, some other methods have been proposed to
model ... | computer science |
1,747 | Progressive EM for Latent Tree Models and Hierarchical Topic Detection | cs.LG | Hierarchical latent tree analysis (HLTA) is recently proposed as a new method
for topic detection. It differs fundamentally from the LDA-based methods in
terms of topic definition, topic-document relationship, and learning method. It
has been shown to discover significantly more coherent topics and better topic
hierarc... | computer science |
1,748 | Improving Decision Analytics with Deep Learning: The Case of Financial
Disclosures | stat.ML | Decision analytics commonly focuses on the text mining of financial news
sources in order to provide managerial decision support and to predict stock
market movements. Existing predictive frameworks almost exclusively apply
traditional machine learning methods, whereas recent research indicates that
traditional machine... | computer science |
1,749 | A Generative Word Embedding Model and its Low Rank Positive Semidefinite
Solution | cs.CL | Most existing word embedding methods can be categorized into Neural Embedding
Models and Matrix Factorization (MF)-based methods. However some models are
opaque to probabilistic interpretation, and MF-based methods, typically solved
using Singular Value Decomposition (SVD), may incur loss of corpus information.
In addi... | computer science |
1,750 | Necessary and Sufficient Conditions and a Provably Efficient Algorithm
for Separable Topic Discovery | cs.LG | We develop necessary and sufficient conditions and a novel provably
consistent and efficient algorithm for discovering topics (latent factors) from
observations (documents) that are realized from a probabilistic mixture of
shared latent factors that have certain properties. Our focus is on the class
of topic models in ... | computer science |
1,751 | Word Representations, Tree Models and Syntactic Functions | cs.CL | Word representations induced from models with discrete latent variables
(e.g.\ HMMs) have been shown to be beneficial in many NLP applications. In this
work, we exploit labeled syntactic dependency trees and formalize the induction
problem as unsupervised learning of tree-structured hidden Markov models.
Syntactic func... | computer science |
1,752 | Word, graph and manifold embedding from Markov processes | cs.CL | Continuous vector representations of words and objects appear to carry
surprisingly rich semantic content. In this paper, we advance both the
conceptual and theoretical understanding of word embeddings in three ways.
First, we ground embeddings in semantic spaces studied in
cognitive-psychometric literature and introdu... | computer science |
1,753 | IllinoisSL: A JAVA Library for Structured Prediction | cs.LG | IllinoisSL is a Java library for learning structured prediction models. It
supports structured Support Vector Machines and structured Perceptron. The
library consists of a core learning module and several applications, which can
be executed from command-lines. Documentation is provided to guide users. In
Comparison to ... | computer science |
1,754 | A Generative Model of Words and Relationships from Multiple Sources | cs.CL | Neural language models are a powerful tool to embed words into semantic
vector spaces. However, learning such models generally relies on the
availability of abundant and diverse training examples. In highly specialised
domains this requirement may not be met due to difficulties in obtaining a
large corpus, or the limit... | computer science |
1,755 | A 'Gibbs-Newton' Technique for Enhanced Inference of Multivariate Polya
Parameters and Topic Models | cs.LG | Hyper-parameters play a major role in the learning and inference process of
latent Dirichlet allocation (LDA). In order to begin the LDA latent variables
learning process, these hyper-parameters values need to be pre-determined. We
propose an extension for LDA that we call 'Latent Dirichlet allocation Gibbs
Newton' (LD... | computer science |
1,756 | Spatial Semantic Scan: Jointly Detecting Subtle Events and their Spatial
Footprint | cs.LG | Many methods have been proposed for detecting emerging events in text streams
using topic modeling. However, these methods have shortcomings that make them
unsuitable for rapid detection of locally emerging events on massive text
streams. We describe Spatially Compact Semantic Scan (SCSS) that has been
developed specif... | computer science |
1,757 | Document Context Language Models | cs.CL | Text documents are structured on multiple levels of detail: individual words
are related by syntax, but larger units of text are related by discourse
structure. Existing language models generally fail to account for discourse
structure, but it is crucial if we are to have language models that reward
coherence and gener... | computer science |
1,758 | Neural Programmer: Inducing Latent Programs with Gradient Descent | cs.LG | Deep neural networks have achieved impressive supervised classification
performance in many tasks including image recognition, speech recognition, and
sequence to sequence learning. However, this success has not been translated to
applications like question answering that may involve complex arithmetic and
logic reason... | computer science |
1,759 | Learning the Dimensionality of Word Embeddings | stat.ML | We describe a method for learning word embeddings with data-dependent
dimensionality. Our Stochastic Dimensionality Skip-Gram (SD-SG) and Stochastic
Dimensionality Continuous Bag-of-Words (SD-CBOW) are nonparametric analogs of
Mikolov et al.'s (2013) well-known 'word2vec' models. Vector dimensionality is
made dynamic b... | computer science |
1,760 | Neural Variational Inference for Text Processing | cs.CL | Recent advances in neural variational inference have spawned a renaissance in
deep latent variable models. In this paper we introduce a generic variational
inference framework for generative and conditional models of text. While
traditional variational methods derive an analytic approximation for the
intractable distri... | computer science |
1,761 | Multi-task Sequence to Sequence Learning | cs.LG | Sequence to sequence learning has recently emerged as a new paradigm in
supervised learning. To date, most of its applications focused on only one task
and not much work explored this framework for multiple tasks. This paper
examines three multi-task learning (MTL) settings for sequence to sequence
models: (a) the onet... | computer science |
1,762 | Order Matters: Sequence to sequence for sets | stat.ML | Sequences have become first class citizens in supervised learning thanks to
the resurgence of recurrent neural networks. Many complex tasks that require
mapping from or to a sequence of observations can now be formulated with the
sequence-to-sequence (seq2seq) framework which employs the chain rule to
efficiently repre... | computer science |
1,763 | Linear Algebraic Structure of Word Senses, with Applications to Polysemy | cs.CL | Word embeddings are ubiquitous in NLP and information retrieval, but it's
unclear what they represent when the word is polysemous, i.e., has multiple
senses. Here it is shown that multiple word senses reside in linear
superposition within the word embedding and can be recovered by simple sparse
coding.
The success of... | computer science |
1,764 | Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic
Modeling in Speech Recognition | cs.CL | We propose to model the acoustic space of deep neural network (DNN)
class-conditional posterior probabilities as a union of low-dimensional
subspaces. To that end, the training posteriors are used for dictionary
learning and sparse coding. Sparse representation of the test posteriors using
this dictionary enables proje... | computer science |
1,765 | From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label
Classification | cs.CL | We propose sparsemax, a new activation function similar to the traditional
softmax, but able to output sparse probabilities. After deriving its
properties, we show how its Jacobian can be efficiently computed, enabling its
use in a network trained with backpropagation. Then, we propose a new smooth
and convex loss func... | computer science |
1,766 | Supervised and Semi-Supervised Text Categorization using LSTM for Region
Embeddings | stat.ML | One-hot CNN (convolutional neural network) has been shown to be effective for
text categorization (Johnson & Zhang, 2015). We view it as a special case of a
general framework which jointly trains a linear model with a non-linear feature
generator consisting of `text region embedding + pooling'. Under this
framework, we... | computer science |
1,767 | Toward Optimal Feature Selection in Naive Bayes for Text Categorization | stat.ML | Automated feature selection is important for text categorization to reduce
the feature size and to speed up the learning process of classifiers. In this
paper, we present a novel and efficient feature selection framework based on
the Information Theory, which aims to rank the features with their
discriminative capacity... | computer science |
1,768 | Spectral Learning for Supervised Topic Models | cs.LG | Supervised topic models simultaneously model the latent topic structure of
large collections of documents and a response variable associated with each
document. Existing inference methods are based on variational approximation or
Monte Carlo sampling, which often suffers from the local minimum defect.
Spectral methods ... | computer science |
1,769 | Characterizing Diseases from Unstructured Text: A Vocabulary Driven
Word2vec Approach | cs.LG | Traditional disease surveillance can be augmented with a wide variety of
real-time sources such as, news and social media. However, these sources are in
general unstructured and, construction of surveillance tools such as
taxonomical correlations and trace mapping involves considerable human
supervision. In this paper,... | computer science |
1,770 | End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF | cs.LG | State-of-the-art sequence labeling systems traditionally require large
amounts of task-specific knowledge in the form of hand-crafted features and
data pre-processing. In this paper, we introduce a novel neutral network
architecture that benefits from both word- and character-level representations
automatically, by usi... | computer science |
1,771 | Integrated Sequence Tagging for Medieval Latin Using Deep Representation
Learning | cs.CL | In this paper we consider two sequence tagging tasks for medieval Latin:
part-of-speech tagging and lemmatization. These are both basic, yet
foundational preprocessing steps in applications such as text re-use detection.
Nevertheless, they are generally complicated by the considerable orthographic
variation which is ty... | computer science |
1,772 | Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders | cs.CL | We present an approach to learning multi-sense word embeddings relying both
on monolingual and bilingual information. Our model consists of an encoder,
which uses monolingual and bilingual context (i.e. a parallel sentence) to
choose a sense for a given word, and a decoder which predicts context words
based on the chos... | computer science |
1,773 | Model Interpolation with Trans-dimensional Random Field Language Models
for Speech Recognition | cs.CL | The dominant language models (LMs) such as n-gram and neural network (NN)
models represent sentence probabilities in terms of conditionals. In contrast,
a new trans-dimensional random field (TRF) LM has been recently introduced to
show superior performances, where the whole sentence is modeled as a random
field. In thi... | computer science |
1,774 | Text-mining the NeuroSynth corpus using Deep Boltzmann Machines | cs.LG | Large-scale automated meta-analysis of neuroimaging data has recently
established itself as an important tool in advancing our understanding of human
brain function. This research has been pioneered by NeuroSynth, a database
collecting both brain activation coordinates and associated text across a large
cohort of neuro... | computer science |
1,775 | Noisy Parallel Approximate Decoding for Conditional Recurrent Language
Model | cs.CL | Recent advances in conditional recurrent language modelling have mainly
focused on network architectures (e.g., attention mechanism), learning
algorithms (e.g., scheduled sampling and sequence-level training) and novel
applications (e.g., image/video description generation, speech recognition,
etc.) On the other hand, ... | computer science |
1,776 | Latent Tree Models for Hierarchical Topic Detection | cs.CL | We present a novel method for hierarchical topic detection where topics are
obtained by clustering documents in multiple ways. Specifically, we model
document collections using a class of graphical models called hierarchical
latent tree models (HLTMs). The variables at the bottom level of an HLTM are
observed binary va... | computer science |
1,777 | Stochastic Structured Prediction under Bandit Feedback | cs.CL | Stochastic structured prediction under bandit feedback follows a learning
protocol where on each of a sequence of iterations, the learner receives an
input, predicts an output structure, and receives partial feedback in form of a
task loss evaluation of the predicted structure. We present applications of
this learning ... | computer science |
1,778 | Increasing the Interpretability of Recurrent Neural Networks Using
Hidden Markov Models | stat.ML | As deep neural networks continue to revolutionize various application
domains, there is increasing interest in making these powerful models more
understandable and interpretable, and narrowing down the causes of good and bad
predictions. We focus on recurrent neural networks (RNNs), state of the art
models in speech re... | computer science |
1,779 | Graph based manifold regularized deep neural networks for automatic
speech recognition | stat.ML | Deep neural networks (DNNs) have been successfully applied to a wide variety
of acoustic modeling tasks in recent years. These include the applications of
DNNs either in a discriminative feature extraction or in a hybrid acoustic
modeling scenario. Despite the rapid progress in this area, a number of
challenges remain ... | computer science |
1,780 | Quantifying and Reducing Stereotypes in Word Embeddings | cs.CL | Machine learning algorithms are optimized to model statistical properties of
the training data. If the input data reflects stereotypes and biases of the
broader society, then the output of the learning algorithm also captures these
stereotypes. In this paper, we initiate the study of gender stereotypes in {\em
word emb... | computer science |
1,781 | Visualizing textual models with in-text and word-as-pixel highlighting | stat.ML | We explore two techniques which use color to make sense of statistical text
models. One method uses in-text annotations to illustrate a model's view of
particular tokens in particular documents. Another uses a high-level,
"words-as-pixels" graphic to display an entire corpus. Together, these methods
offer both zoomed-i... | computer science |
1,782 | A Probabilistic Generative Grammar for Semantic Parsing | cs.CL | We present a framework that couples the syntax and semantics of natural
language sentences in a generative model, in order to develop a semantic parser
that jointly infers the syntactic, morphological, and semantic representations
of a given sentence under the guidance of background knowledge. To generate a
sentence in... | computer science |
1,783 | Toward Interpretable Topic Discovery via Anchored Correlation
Explanation | stat.ML | Many predictive tasks, such as diagnosing a patient based on their medical
chart, are ultimately defined by the decisions of human experts. Unfortunately,
encoding experts' knowledge is often time consuming and expensive. We propose a
simple way to use fuzzy and informal knowledge from experts to guide discovery
of int... | computer science |
1,784 | Neural Semantic Encoders | cs.LG | We present a memory augmented neural network for natural language
understanding: Neural Semantic Encoders. NSE is equipped with a novel memory
update rule and has a variable sized encoding memory that evolves over time and
maintains the understanding of input sequences through read}, compose and write
operations. NSE c... | computer science |
1,785 | Neural Tree Indexers for Text Understanding | cs.CL | Recurrent neural networks (RNNs) process input text sequentially and model
the conditional transition between word tokens. In contrast, the advantages of
recursive networks include that they explicitly model the compositionality and
the recursive structure of natural language. However, the current recursive
architectur... | computer science |
1,786 | Imitation Learning with Recurrent Neural Networks | cs.CL | We present a novel view that unifies two frameworks that aim to solve
sequential prediction problems: learning to search (L2S) and recurrent neural
networks (RNN). We point out equivalences between elements of the two
frameworks. By complementing what is missing from one framework comparing to
the other, we introduce a... | computer science |
1,787 | TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for
Automatic Measurement in MOOCs | cs.LG | This paper explores the suitability of using automatically discovered topics
from MOOC discussion forums for modelling students' academic abilities. The
Rasch model from psychometrics is a popular generative probabilistic model that
relates latent student skill, latent item difficulty, and observed student-item
respons... | computer science |
1,788 | Convolutional Neural Networks for Text Categorization: Shallow
Word-level vs. Deep Character-level | cs.CL | This paper reports the performances of shallow word-level convolutional
neural networks (CNN), our earlier work (2015), on the eight datasets with
relatively large training data that were used for testing the very deep
character-level CNN in Conneau et al. (2016). Our findings are as follows. The
shallow word-level CNN... | computer science |
1,789 | Ask the GRU: Multi-Task Learning for Deep Text Recommendations | stat.ML | In a variety of application domains the content to be recommended to users is
associated with text. This includes research papers, movies with associated
plot summaries, news articles, blog posts, etc. Recommendation approaches based
on latent factor models can be extended naturally to leverage text by employing
an exp... | computer science |
1,790 | Nonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor
Processes | stat.ML | The Dirichlet process and its extension, the Pitman-Yor process, are
stochastic processes that take probability distributions as a parameter. These
processes can be stacked up to form a hierarchical nonparametric Bayesian
model. In this article, we present efficient methods for the use of these
processes in this hierar... | computer science |
1,791 | Semi-supervised Learning with Sparse Autoencoders in Phone
Classification | stat.ML | We propose the application of a semi-supervised learning method to improve
the performance of acoustic modelling for automatic speech recognition based on
deep neural net- works. As opposed to unsupervised initialisation followed by
supervised fine tuning, our method takes advantage of both unlabelled and
labelled data... | computer science |
1,792 | Latent Sequence Decompositions | stat.ML | We present the Latent Sequence Decompositions (LSD) framework. LSD decomposes
sequences with variable lengthed output units as a function of both the input
sequence and the output sequence. We present a training algorithm which samples
valid extensions and an approximate decoding algorithm. We experiment with the
Wall ... | computer science |
1,793 | Simultaneous Learning of Trees and Representations for Extreme
Classification and Density Estimation | stat.ML | We consider multi-class classification where the predictor has a hierarchical
structure that allows for a very large number of labels both at train and test
time. The predictive power of such models can heavily depend on the structure
of the tree, and although past work showed how to learn the tree structure, it
expect... | computer science |
1,794 | End-to-End Training Approaches for Discriminative Segmental Models | cs.CL | Recent work on discriminative segmental models has shown that they can
achieve competitive speech recognition performance, using features based on
deep neural frame classifiers. However, segmental models can be more
challenging to train than standard frame-based approaches. While some segmental
models have been success... | computer science |
1,795 | Geometry of Polysemy | cs.CL | Vector representations of words have heralded a transformational approach to
classical problems in NLP; the most popular example is word2vec. However, a
single vector does not suffice to model the polysemous nature of many
(frequent) words, i.e., words with multiple meanings. In this paper, we propose
a three-fold appr... | computer science |
1,796 | Tying Word Vectors and Word Classifiers: A Loss Framework for Language
Modeling | cs.LG | Recurrent neural networks have been very successful at predicting sequences
of words in tasks such as language modeling. However, all such models are based
on the conventional classification framework, where the model is trained
against one-hot targets, and each word is represented both as an input and as
an output in ... | computer science |
1,797 | Automatic Node Selection for Deep Neural Networks using Group Lasso
Regularization | cs.CL | We examine the effect of the Group Lasso (gLasso) regularizer in selecting
the salient nodes of Deep Neural Network (DNN) hidden layers by applying a
DNN-HMM hybrid speech recognizer to TED Talks speech data. We test two types of
gLasso regularization, one for outgoing weight vectors and another for incoming
weight vec... | computer science |
1,798 | Unsupervised Learning for Lexicon-Based Classification | cs.LG | In lexicon-based classification, documents are assigned labels by comparing
the number of words that appear from two opposed lexicons, such as positive and
negative sentiment. Creating such words lists is often easier than labeling
instances, and they can be debugged by non-experts if classification
performance is unsa... | computer science |
1,799 | Bidirectional LSTM-CRF for Clinical Concept Extraction | stat.ML | Automated extraction of concepts from patient clinical records is an
essential facilitator of clinical research. For this reason, the 2010 i2b2/VA
Natural Language Processing Challenges for Clinical Records introduced a
concept extraction task aimed at identifying and classifying concepts into
predefined categories (i.... | computer science |
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