Topic Distiller:distilling semantic topics from documents

Abstract. This thesis details the design and implementation of a system that can find relevant and latent semantic topics from textual documents. The design of this system, named Topic Distiller, is inspired by research conducted on automatic keyphrase extraction and automatic topic labeling, and it employs entity linking and knowledge bases to reduce text documents to their semantic topics. The Topic Distiller is evaluated using methods and datasets used in information retrieval and automatic keyphrase extraction. On top of the common datasets used in the literature three additional datasets are created to evaluate the system. The evaluation reveals that the Topic Distiller is able to find relevant and latent topics from textual documents, beating the state-of-the-art automatic keyphrase methods in performance when used on news articles and social media posts.Semanttisten aiheiden suodattaminen dokumenteista. Tiivistelmä. Tässä diplomityössä tarkastellaan järjestelmää, joka pystyy löytämään tekstistä relevantteja ja piileviä semanttisia aihealueita, sekä kyseisen järjestelmän suunnittelua ja implementaatiota. Tämän Topic Distiller -järjestelmän suunnittelu ammentaa inspiraatiota automaattisen termintunnistamisen ja automaattisen aiheiden nimeämisen tutkimuksesta sekä hyödyntää automaattista semanttista annotointia ja tietämyskantoja tekstin aihealueiden löytämisessä. Topic Distiller -järjestelmän suorituskykyä mitataan hyödyntämällä kirjallisuudessa paljon käytettyjä automaattisen termintunnistamisen evaluontimenetelmiä ja aineistoja. Näiden yleisten aineistojen lisäksi esittelemme kolme uutta aineistoa, jotka on luotu Topic Distiller -järjestelmän arviointia varten. Evaluointi tuo ilmi, että Topic Distiller kykenee löytämään relevantteja ja piileviä aiheita tekstistä. Se päihittää kirjallisuuden viimeisimmät automaattisen termintunnistamisen menetelmät suorituskyvyssä, kun sitä käytetään uutisartikkelien sekä sosiaalisen median julkaisujen analysointiin

Deep keyphrase generation

Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at https://github.com/memray/seq2seqkeyphrase

Methodologies for the Automatic Location of Academic and Educational Texts on the Internet

Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as ‘appropriate’ to a given database, a problem only solved by complex text content analysis. This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined

Keywords at Work: Investigating Keyword Extraction in Social Media Applications

This dissertation examines a long-standing problem in Natural Language Processing (NLP) -- keyword extraction -- from a new angle. We investigate how keyword extraction can be formulated on social media data, such as emails, product reviews, student discussions, and student statements of purpose. We design novel graph-based features for supervised and unsupervised keyword extraction from emails, and use the resulting system with success to uncover patterns in a new dataset -- student statements of purpose. Furthermore, the system is used with new features on the problem of usage expression extraction from product reviews, where we obtain interesting insights. The system while used on student discussions, uncover new and exciting patterns. While each of the above problems is conceptually distinct, they share two key common elements -- keywords and social data. Social data can be messy, hard-to-interpret, and not easily amenable to existing NLP resources. We show that our system is robust enough in the face of such challenges to discover useful and important patterns. We also show that the problem definition of keyword extraction itself can be expanded to accommodate new and challenging research questions and datasets.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145929/1/lahiri_1.pd