Scatteract: Automated extraction of data from scatter plots

Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set.Comment: Submitted to ECML PKDD 2017 proceedings, 16 page

Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction: Learning from Labeled and Unlabeled Data.

A journal article is often accompanied by a list of keyphrases, composed of about five to fifteen important words and phrases that capture the article’s main topics. Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. Good performance on this task has been obtained by approaching it as a supervised learning problem. An input document is treated as a set of candidate phrases that must be classified as either keyphrases or non-keyphrases. To classify a candidate phrase as a keyphrase, the most important features (attributes) appear to be the frequency and location of the candidate phrase in the document. Recent work has demonstrated that it is also useful to know the frequency of the candidate phrase as a manually assigned keyphrase for other documents in the same domain as the given document (e.g., the domain of computer science). Unfortunately, this keyphrase-frequency feature is domain-specific (the learning process must be repeated for each new domain) and training-intensive (good performance requires a relatively large number of training documents in the given domain, with manually assigned keyphrases). The aim of the work described here is to remove these limitations. In this paper, I introduce new features that are conceptually related to keyphrase-frequency and I present experiments that show that the new features result in improved keyphrase extraction, although they are neither domain-specific nor training-intensive. The new features are generated by issuing queries to a Web search engine, based on the candidate phrases in the input document. The feature values are calculated from the number of hits for the queries (the number of matching Web pages). In essence, these new features are derived by mining lexical knowledge from a very large collection of unlabeled data, consisting of approximately 350 million Web pages without manually assigned keyphrases

Coherent Keyphrase Extraction via Web Mining

Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. A limitation of previous keyphrase extraction algorithms is that the selected keyphrases are occasionally incoherent. That is, the majority of the output keyphrases may fit together well, but there may be a minority that appear to be outliers, with no clear semantic relation to the majority or to each other. This paper presents enhancements to the Kea keyphrase extraction algorithm that are designed to increase the coherence of the extracted keyphrases. The approach is to use the degree of statistical association among candidate keyphrases as evidence that they may be semantically related. The statistical association is measured using web mining. Experiments demonstrate that the enhancements improve the quality of the extracted keyphrases. Furthermore, the enhancements are not domain-specific: the algorithm generalizes well when it is trained on one domain (computer science documents) and tested on another (physics documents).Comment: 6 pages, related work available at http://purl.org/peter.turney

trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R

Research is an incremental, iterative process, with new results relying and building upon previous ones. Scientists need to find, retrieve, understand, and verify results in order to confidently extend them, even when the results are their own. We present the trackr framework for organizing, automatically annotating, discovering, and retrieving results. We identify sources of automatically extractable metadata for computational results, and we define an extensible system for organizing, annotating, and searching for results based on these and other metadata. We present an open-source implementation of these concepts for plots, computational artifacts, and woven dynamic reports generated in the R statistical computing language

Learning to Extract Keyphrases from Text

Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft?s Word 97 and Verity?s Search 97, includes algorithms that automatically extract keyphrases from documents. In this paper, we approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft?s Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity?s Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97)