176 research outputs found
Exploratory Analysis of Highly Heterogeneous Document Collections
We present an effective multifaceted system for exploratory analysis of
highly heterogeneous document collections. Our system is based on intelligently
tagging individual documents in a purely automated fashion and exploiting these
tags in a powerful faceted browsing framework. Tagging strategies employed
include both unsupervised and supervised approaches based on machine learning
and natural language processing. As one of our key tagging strategies, we
introduce the KERA algorithm (Keyword Extraction for Reports and Articles).
KERA extracts topic-representative terms from individual documents in a purely
unsupervised fashion and is revealed to be significantly more effective than
state-of-the-art methods. Finally, we evaluate our system in its ability to
help users locate documents pertaining to military critical technologies buried
deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery
and Data Minin
Topic Similarity Networks: Visual Analytics for Large Document Sets
We investigate ways in which to improve the interpretability of LDA topic
models by better analyzing and visualizing their outputs. We focus on examining
what we refer to as topic similarity networks: graphs in which nodes represent
latent topics in text collections and links represent similarity among topics.
We describe efficient and effective approaches to both building and labeling
such networks. Visualizations of topic models based on these networks are shown
to be a powerful means of exploring, characterizing, and summarizing large
collections of unstructured text documents. They help to "tease out"
non-obvious connections among different sets of documents and provide insights
into how topics form larger themes. We demonstrate the efficacy and
practicality of these approaches through two case studies: 1) NSF grants for
basic research spanning a 14 year period and 2) the entire English portion of
Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData
2014
Human-competitive automatic topic indexing
Topic indexing is the task of identifying the main topics covered by a document. These are useful for many purposes: as subject headings in libraries, as keywords in academic publications and as tags on the web. Knowing a document's topics helps people judge its relevance quickly. However, assigning topics manually is labor intensive. This thesis shows how to generate them automatically in a way that competes with human performance.
Three kinds of indexing are investigated: term assignment, a task commonly performed by librarians, who select topics from a controlled vocabulary; tagging, a popular activity of web users, who choose topics freely; and a new method of keyphrase extraction, where topics are equated to Wikipedia article names. A general two-stage algorithm is introduced that first selects candidate topics and then ranks them by significance based on their properties. These properties draw on statistical, semantic, domain-specific and encyclopedic knowledge. They are combined using a machine learning algorithm that models human indexing behavior from examples.
This approach is evaluated by comparing automatically generated topics to those assigned by professional indexers, and by amateurs. We claim that the algorithm is human-competitive because it chooses topics that are as consistent with those assigned by humans as their topics are with each other. The approach is generalizable, requires little training data and applies across different domains and languages
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
There is a perennial need in the online advertising industry to refresh ad
creatives, i.e., images and text used for enticing online users towards a
brand. Such refreshes are required to reduce the likelihood of ad fatigue among
online users, and to incorporate insights from other successful campaigns in
related product categories. Given a brand, to come up with themes for a new ad
is a painstaking and time consuming process for creative strategists.
Strategists typically draw inspiration from the images and text used for past
ad campaigns, as well as world knowledge on the brands. To automatically infer
ad themes via such multimodal sources of information in past ad campaigns, we
propose a theme (keyphrase) recommender system for ad creative strategists. The
theme recommender is based on aggregating results from a visual question
answering (VQA) task, which ingests the following: (i) ad images, (ii) text
associated with the ads as well as Wikipedia pages on the brands in the ads,
and (iii) questions around the ad. We leverage transformer based cross-modality
encoders to train visual-linguistic representations for our VQA task. We study
two formulations for the VQA task along the lines of classification and
ranking; via experiments on a public dataset, we show that cross-modal
representations lead to significantly better classification accuracy and
ranking precision-recall metrics. Cross-modal representations show better
performance compared to separate image and text representations. In addition,
the use of multimodal information shows a significant lift over using only
textual or visual information.Comment: 7 pages, 8 figures, 2 tables, accepted by The Web Conference 202
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