20,192 research outputs found
How Many Topics? Stability Analysis for Topic Models
Topic modeling refers to the task of discovering the underlying thematic
structure in a text corpus, where the output is commonly presented as a report
of the top terms appearing in each topic. Despite the diversity of topic
modeling algorithms that have been proposed, a common challenge in successfully
applying these techniques is the selection of an appropriate number of topics
for a given corpus. Choosing too few topics will produce results that are
overly broad, while choosing too many will result in the "over-clustering" of a
corpus into many small, highly-similar topics. In this paper, we propose a
term-centric stability analysis strategy to address this issue, the idea being
that a model with an appropriate number of topics will be more robust to
perturbations in the data. Using a topic modeling approach based on matrix
factorization, evaluations performed on a range of corpora show that this
strategy can successfully guide the model selection process.Comment: Improve readability of plots. Add minor clarification
Weakly-Supervised Neural Text Classification
Deep neural networks are gaining increasing popularity for the classic text
classification task, due to their strong expressive power and less requirement
for feature engineering. Despite such attractiveness, neural text
classification models suffer from the lack of training data in many real-world
applications. Although many semi-supervised and weakly-supervised text
classification models exist, they cannot be easily applied to deep neural
models and meanwhile support limited supervision types. In this paper, we
propose a weakly-supervised method that addresses the lack of training data in
neural text classification. Our method consists of two modules: (1) a
pseudo-document generator that leverages seed information to generate
pseudo-labeled documents for model pre-training, and (2) a self-training module
that bootstraps on real unlabeled data for model refinement. Our method has the
flexibility to handle different types of weak supervision and can be easily
integrated into existing deep neural models for text classification. We have
performed extensive experiments on three real-world datasets from different
domains. The results demonstrate that our proposed method achieves inspiring
performance without requiring excessive training data and outperforms baseline
methods significantly.Comment: CIKM 2018 Full Pape
Distantly Labeling Data for Large Scale Cross-Document Coreference
Cross-document coreference, the problem of resolving entity mentions across
multi-document collections, is crucial to automated knowledge base construction
and data mining tasks. However, the scarcity of large labeled data sets has
hindered supervised machine learning research for this task. In this paper we
develop and demonstrate an approach based on ``distantly-labeling'' a data set
from which we can train a discriminative cross-document coreference model. In
particular we build a dataset of more than a million people mentions extracted
from 3.5 years of New York Times articles, leverage Wikipedia for distant
labeling with a generative model (and measure the reliability of such
labeling); then we train and evaluate a conditional random field coreference
model that has factors on cross-document entities as well as mention-pairs.
This coreference model obtains high accuracy in resolving mentions and entities
that are not present in the training data, indicating applicability to
non-Wikipedia data. Given the large amount of data, our work is also an
exercise demonstrating the scalability of our approach.Comment: 16 pages, submitted to ECML 201
Online Unsupervised Multi-view Feature Selection
In the era of big data, it is becoming common to have data with multiple
modalities or coming from multiple sources, known as "multi-view data".
Multi-view data are usually unlabeled and come from high-dimensional spaces
(such as language vocabularies), unsupervised multi-view feature selection is
crucial to many applications. However, it is nontrivial due to the following
challenges. First, there are too many instances or the feature dimensionality
is too large. Thus, the data may not fit in memory. How to select useful
features with limited memory space? Second, how to select features from
streaming data and handles the concept drift? Third, how to leverage the
consistent and complementary information from different views to improve the
feature selection in the situation when the data are too big or come in as
streams? To the best of our knowledge, none of the previous works can solve all
the challenges simultaneously. In this paper, we propose an Online unsupervised
Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming
multi-view data in an online fashion. OMVFS embeds unsupervised feature
selection into a clustering algorithm via NMF with sparse learning. It further
incorporates the graph regularization to preserve the local structure
information and help select discriminative features. Instead of storing all the
historical data, OMVFS processes the multi-view data chunk by chunk and
aggregates all the necessary information into several small matrices. By using
the buffering technique, the proposed OMVFS can reduce the computational and
storage cost while taking advantage of the structure information. Furthermore,
OMVFS can capture the concept drifts in the data streams. Extensive experiments
on four real-world datasets show the effectiveness and efficiency of the
proposed OMVFS method. More importantly, OMVFS is about 100 times faster than
the off-line methods
Taking the bite out of automated naming of characters in TV video
We investigate the problem of automatically labelling appearances of characters in TV or film material
with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying
when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ‘‘Buffy the Vampire Slayer”
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