Using Social Media to Promote STEM Education: Matching College Students with Role Models

STEM (Science, Technology, Engineering, and Mathematics) fields have become increasingly central to U.S. economic competitiveness and growth. The shortage in the STEM workforce has brought promoting STEM education upfront. The rapid growth of social media usage provides a unique opportunity to predict users' real-life identities and interests from online texts and photos. In this paper, we propose an innovative approach by leveraging social media to promote STEM education: matching Twitter college student users with diverse LinkedIn STEM professionals using a ranking algorithm based on the similarities of their demographics and interests. We share the belief that increasing STEM presence in the form of introducing career role models who share similar interests and demographics will inspire students to develop interests in STEM related fields and emulate their models. Our evaluation on 2,000 real college students demonstrated the accuracy of our ranking algorithm. We also design a novel implementation that recommends matched role models to the students.Comment: 16 pages, 8 figures, accepted by ECML/PKDD 2016, Industrial Trac

Augmenting Latent Dirichlet Allocation and Rank Threshold Detection with Ontologies

In an ever-increasing data rich environment, actionable information must be extracted, filtered, and correlated from massive amounts of disparate often free text sources. The usefulness of the retrieved information depends on how we accomplish these steps and present the most relevant information to the analyst. One method for extracting information from free text is Latent Dirichlet Allocation (LDA), a document categorization technique to classify documents into cohesive topics. Although LDA accounts for some implicit relationships such as synonymy (same meaning) it often ignores other semantic relationships such as polysemy (different meanings), hyponym (subordinate), meronym (part of), and troponomys (manner). To compensate for this deficiency, we incorporate explicit word ontologies, such as WordNet, into the LDA algorithm to account for various semantic relationships. Experiments over the 20 Newsgroups, NIPS, OHSUMED, and IED document collections demonstrate that incorporating such knowledge improves perplexity measure over LDA alone for given parameters. In addition, the same ontology augmentation improves recall and precision results for user queries

情報検索における意味的ギャップの解消 : トピックモデルを用いた先進的画像探索

Tohoku University徳山豪課

Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet

This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature

Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Extraction and Classification of App Features from App Reviews

Aasta aastalt on kasvanud bioinformaatikas kasutatavate rakenduste arv.Selle tulemusena on konkreetse ülesande lahendamiseks sobiliku rakenduse leidmine muutunud keerukaks ülesandeks.Rakenduste kirjelduste paremaks süstematiseerimiseks ja otsitavaks muutmiseks on kasutusele võetud erinevaid märksõnade ontoloogiaid. Hetkel annoteeritakse kirjeldusi käsitsi, mis on ajamahukas ning ei anna alati õigeid tulemusi.Antud töös kirjeldame uut annoteerimismeetodit, mis pakub automaatselt välja ühe või mitu märksõna kasutades selleks vaid tööriista vabatekstilist kirjeldust.Selleks kasutab meie meetod uusimaid loomuliku keele töötlemise meetodeid nagu Dirichlet' peitlahutus (latent Dirichlet allocation) ja sõnade vektoresitust (word2vec).Esmane võrdlus meie poolt välja pakutud algoritmi ja käsitsi saadud märgendusega näitab, et tulemused on paljulubavad.The number of tools for bioinformatics is constantly increasing. To organize the available information and to facilitate the search, different ontologies are used. Today annotation of new descriptions is done manually, which is time-consuming and not always correct. We proposed a new annotation method, which, based on the description of the tool, offers one or more annotation labels in accordance with the ontology. In our method, we applied modern methods of natural language processing, such as latent Dirichlet allocation and word2vec. We compared the manual annotation labels with the labels obtained by using our algorithm and the first results look auspicious