DOCSLIB.ORG

Package 'Qdap'

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Package 'Qdap' Package ‘qdap’ March 13, 2013 Type Package Title Bridging the gap between qualitative data and quantitative analysis Version 0.2.1 Date 2012-05-09 Author Tyler Rinker Maintainer Tyler Rinker <[email protected]> Depends R (>= 2.15), gdata, ggplot2 (>= 0.9.2), grid, reports, scales Imports gridExtra, chron, grDevices, RColorBrewer, igraph, tm,wordcloud, venneuler, openNLPmod- els.en, Snowball, gplots,gridExtra, openNLP, plotrix, XML, RCurl, reshape2, parallel,tools Suggests plyr, koRpus LazyData TRUE Description This package automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse including fre- quency counts of sentence types, words,sentence, turns of talk, syllable counts and other assorted analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This provides the user with a more efficient and targeted analysis. Note qdap is not compiled for Mac users. Installation instructions for Mac user or other OS users having difficulty installing qdap please visit: http://trinker.github.com/qdap_install/installation License GPL-2 URL http://trinker.github.com/qdap/ BugReports http://github.com/trinker/qdap/issues 1 2 R topics documented: Collate ’adjacency_matrix.R’ ’all_words.R’’automated_readability_index.R’ ’bag.o.words.R’ ’blank2NA.R’’bracketX.R’ ’capitalizer.R’ ’clean.R’ ’cm_bidist.R’’cm_code.blank.R’ ’cm_code.combine.R’ ’cm_code.exclude.R’’cm_code.overlap.R’ ’cm_code.transform.R’ ’cm_combine.dummy.R’’cm_d2l.R’ ’cm_debug.R’ ’cm_debug2.R’ ’cm_describe.R’’cm_df.fill.R’ ’cm_df.temp.R’ ’cm_df.transcript.R’’cm_df.transform.R’ ’cm_df2long.R’ ’cm_distance.R’’cm_dummy2long.R’ ’cm_long2dummy.R’ ’cm_r2l.R’’cm_range.temp.R’ ’cm_range2long.R’ ’cm_se2vect.R’ ’cm_t2l.R’’cm_time.temp.R’ ’cm_time2long.R’ ’col.fn.R’ ’colSplit.R’’colsplit2df.R’ ’combine_tot.R’ ’common.R’ ’dir_map.R’’dissimilarity.R’ ’distTab.R’ ’diversity.R’ ’duplicates.R’’end_inc.R’ ’end_mark.R’ ’exclude.R’ ’formality.R’’freqTab2words.R’ ’gantt.R’ ’gantt_plot.R’ ’gantt_rep.R’’gantt_wrap.R’ ’gradient_cloud.R’ ’hash.R’ ’hms2sec.R’’htruncdf.R’ ’imperative.R’ ’incomplete.replace.R’ ’is.dp.R’’key_merge.R’ ’kullback.leibler.R’ ’left.just.R’ ’lookup.R’’mcsv_r.R’ ’merge_all.R’ ’multigsub.R’ ’multiscale.R’ ’NAer.R’’ncutWORD.R’ ’ncutWORDS.R’ ’new_project.R’ ’numbtext.R’’numfor.R’ ’outlier.detect.R’ ’outlier.labeler.R’ ’partition.R’’paste2.R’ ’polarity.R’ ’pos.R’ ’potential_NA.R’ ’prop.R’’qcombine.R’ ’qcv.R’ ’qda.handler.R’ ’qdap- package.R’ ’qheat.R’’qprep.R’ ’question_type.R’ ’rank_freq_plot.R’ ’raw_pro_comb.R’’read.docx.R’ ’read.transcript.R’ ’reducer.R’ ’reduceWORDS.R’’replace_abbreviation.R’ ’replace_contraction.R’’replace_number.R’ ’replace_symbol.R’ ’replacer.R’ ’rm_row.R’’scrubber.R’ ’Search.R’ ’sec2hms.R’ ’sentSplit.R’’space_fill.R’ ’spaste.R’ ’speakerSplit.R’ ’stemmer.R’’stopwords.R’ ’strip.R’ ’strWrap.R’ ’syllable.sum.R’’synonyms.R’ ’term.count.R’ ’term.find.R’ ’termco.c.R’’termco.h.R’ ’termco.p.R’ ’termco.R’ ’termco2short.term.R’’tester.R’ ’text2color.R’ ’textLISTER.R’ ’trans.cloud.R’’trans.venn.R’ ’Trim.R’ ’unblanker.R’ ’url_dl.R’ ’v.outer.R’’wfm.R’ ’word.count.R’ ’word.network.plot.R’ ’word_associate.R’’word_diff_list.R’ ’word_list.R’ ’word_stats.R’ ’words.R’’xnoy.R’ NeedsCompilation no Repository CRAN Date/Publication 2013-03-13 08:07:10 R topics documented: abbreviations . .5 action.verbs . .6 adjacency_matrix . .6 adverb . .7 all_words . .8 automated_readability_index . .9 bag.o.words . 11 blank2NA . 12 bracketX . 13 BuckleySaltonSWL . 15 capitalizer . 16 clean . 17 cm_code.blank . 17 cm_code.combine . 19 cm_code.exclude . 21 cm_code.overlap . 22 cm_code.transform . 24 cm_combine.dummy . 26 cm_df.fill . 27 cm_df.temp . 29 cm_df.transcript . 30 cm_df2long . 31 cm_distance . 32 cm_dummy2long . 34 cm_long2dummy . 35 cm_range.temp . 37 cm_range2long . 38 cm_time.temp . 39 cm_time2long . 40 colSplit . 41 colsplit2df . 42 common . 43 common.list . 44 contractions . 45 DATA ............................................ 45 DATA2 ........................................... 46 DICTIONARY . 46 R topics documented: 3 dir_map . 47 dissimilarity . 48 distTab . 49 diversity . 50 duplicates . 51 emoticon . 52 end_inc . 52 end_mark . 53 env.syl............................................ 54 env.syn . 54 exclude . 55 formality . 56 gantt............................................. 58 gantt_plot . 60 gantt_rep . 61 gantt_wrap . 62 gradient_cloud . 65 hash............................................. 67 hms2sec . 68 htruncdf . 69 imperative . 70 incomplete.replace . 71 increase.amplification.words . 72 interjections . 72 key_merge . 73 kullback.leibler . 74 labMT . 75 left.just . 75 lookup . 76 mcsv_r . 77 mraja1 . 79 mraja1spl . 79 multigsub . 80 multiscale . 81 NAer............................................. 82 negation.words . 83 negative.words . 83 new_project . 84 OnixTxtRetToolkitSWL1 . 85 outlier.detect . 86 outlier.labeler . 87 paste2 . 87 plot.character.table . 88 plot.diversity . 89 plot.formality . 89 plot.polarity . 90 plot.pos.by ..
Load more

Recommended publications
  • An Analysis of Dilemmas in English Composition Among Asian College Students Shuko Nawa University of North Florida
    UNF Digital Commons UNF Graduate Theses and Dissertations Student Scholarship 1995 An Analysis Of Dilemmas In English Composition Among Asian College Students Shuko Nawa University of North Florida Suggested Citation Nawa, Shuko, "An Analysis Of Dilemmas In English Composition Among Asian College Students" (1995). UNF Graduate Theses and Dissertations. 83. https://digitalcommons.unf.edu/etd/83 This Master's Thesis is brought to you for free and open access by the Student Scholarship at UNF Digital Commons. It has been accepted for inclusion in UNF Graduate Theses and Dissertations by an authorized administrator of UNF Digital Commons. For more information, please contact Digital Projects. © 1995 All Rights Reserved AN ANALYSIS OF DILEMMAS IN ENGLISH COMPOSITION AMONG ASIAN COLLEGE STUDENTS by Shuko Nawa A thesis submitted to the Department of Curriculum and Instruction in partial fulfillment of the requirements for the degree of Master of Education UNIVERSITY OF NORTH FLORIDA COLLEGE OF EDUCATION AND HUMAN SERVICES May, 1995 Unpublished work © Shuko Nawa Certificate of Approval The thesis ofShuko Nawa is approved: (Date) Signature Deleted Signature Deleted Signature Deleted Accepted for the Division: Signature Deleted Chairperson Accepted for the College: Signature Deleted Accepted for the University Signature Deleted Dedication and Acknowledgement This study is dedicated to the researcher's father, Nobuhide Nawa, who taught the importance of learning and exhibited continuous efforts to become a better person through his life. The researcher gratefully acknowledges the assistance of Dr. Priscilla VanZandt, Dr. Patricia Ellis, and Mrs. Mary Thornton in preparing research materials. Conscientious reviewing by Miss Suzanne Fuss is also deeply appreciated. The researcher would like to extend special thanks to Dr.
    [Show full text]
  • Package 'Tidytext'
    Package ‘tidytext’ February 19, 2018 Type Package Title Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools Version 0.1.7 Description Text mining for word processing and sentiment analysis using 'dplyr', 'ggplot2', and other tidy tools. License MIT + file LICENSE LazyData TRUE URL http://github.com/juliasilge/tidytext BugReports http://github.com/juliasilge/tidytext/issues RoxygenNote 6.0.1 Depends R (>= 2.10) Imports rlang, dplyr, stringr, hunspell, broom, Matrix, tokenizers, janeaustenr, purrr (>= 0.1.1), methods, stopwords Suggests readr, tidyr, XML, tm, quanteda, knitr, rmarkdown, ggplot2, reshape2, wordcloud, topicmodels, NLP, scales, gutenbergr, testthat, mallet, stm VignetteBuilder knitr NeedsCompilation no Author Gabriela De Queiroz [ctb], Oliver Keyes [ctb], David Robinson [aut], Julia Silge [aut, cre] Maintainer Julia Silge <[email protected]> Repository CRAN Date/Publication 2018-02-19 19:46:02 UTC 1 2 bind_tf_idf R topics documented: bind_tf_idf . .2 cast_sparse . .3 cast_tdm . .4 corpus_tidiers . .5 dictionary_tidiers . .6 get_sentiments . .6 get_stopwords . .7 lda_tidiers . .8 mallet_tidiers . 10 nma_words . 12 parts_of_speech . 12 sentiments . 13 stm_tidiers . 14 stop_words . 16 tdm_tidiers . 17 tidy.Corpus . 18 tidytext . 19 tidy_triplet . 19 unnest_tokens . 20 Index 22 bind_tf_idf Bind the term frequency and inverse document frequency of a tidy text dataset to the dataset Description Calculate and bind the term frequency and inverse document frequency of a tidy text dataset, along with the product, tf-idf, to
    [Show full text]
  • A Customizable Speech Practice Application for People Who Stutter
    Rollins College Rollins Scholarship Online Honors Program Theses Spring 2021 A Customizable Speech Practice Application for People Who Stutter Eric Grimm [email protected] Nikola Vuckovic Follow this and additional works at: https://scholarship.rollins.edu/honors Part of the Other Computer Sciences Commons Recommended Citation Grimm, Eric and Vuckovic, Nikola, "A Customizable Speech Practice Application for People Who Stutter" (2021). Honors Program Theses. 154. https://scholarship.rollins.edu/honors/154 This Open Access is brought to you for free and open access by Rollins Scholarship Online. It has been accepted for inclusion in Honors Program Theses by an authorized administrator of Rollins Scholarship Online. For more information, please contact [email protected]. 1 A Customizable Speech Practice Application for People Who Stutter Eric Grimm and Nikola Vuckovic Rollins College 2 Abstract Stuttering is a speech impediment that often requires speech therapy to curb the symptoms. In speech therapy, people who stutter (PWS) learn techniques that they can use to improve their fluency. PWS often practice their techniques extensively in order to maintain fluent speech. Many listen to audio recordings to practice where a single word or sentence is played on the recording and then there is a pause, giving the user a chance to say the word(s) to practice. This style of practice is not customizable and is repetitive since the contents do not change. Thus, we have developed an application for iPhone that uses a text-to-speech API to read single words and sentences to PWS, so that they can practice their techniques. Each practice mode is customizable in that the user can choose to practice certain sounds that they struggle with, and the app will respond by choosing words that start with the desired sound or generate sentences that contain several words that start with the desired sound.
    [Show full text]
  • Think Python
    Think Python How to Think Like a Computer Scientist 2nd Edition, Version 2.4.0 Think Python How to Think Like a Computer Scientist 2nd Edition, Version 2.4.0 Allen Downey Green Tea Press Needham, Massachusetts Copyright © 2015 Allen Downey. Green Tea Press 9 Washburn Ave Needham MA 02492 Permission is granted to copy, distribute, and/or modify this document under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported License, which is available at http: //creativecommons.org/licenses/by-nc/3.0/. The original form of this book is LATEX source code. Compiling this LATEX source has the effect of gen- erating a device-independent representation of a textbook, which can be converted to other formats and printed. http://www.thinkpython2.com The LATEX source for this book is available from Preface The strange history of this book In January 1999 I was preparing to teach an introductory programming class in Java. I had taught it three times and I was getting frustrated. The failure rate in the class was too high and, even for students who succeeded, the overall level of achievement was too low. One of the problems I saw was the books. They were too big, with too much unnecessary detail about Java, and not enough high-level guidance about how to program. And they all suffered from the trap door effect: they would start out easy, proceed gradually, and then somewhere around Chapter 5 the bottom would fall out. The students would get too much new material, too fast, and I would spend the rest of the semester picking up the pieces.
    [Show full text]
  • SPELLING out P a UNIFIED SYNTAX of AFRIKAANS ADPOSITIONS and V-PARTICLES by Erin Pretorius Dissertation Presented for the Degree
    SPELLING OUT P A UNIFIED SYNTAX OF AFRIKAANS ADPOSITIONS AND V-PARTICLES by Erin Pretorius Dissertation presented for the degree of Doctor of Philosophy in the Faculty of Arts and Social Sciences at Stellenbosch University Supervisors Prof Theresa Biberauer (Stellenbosch University) Prof Norbert Corver (University of Utrecht) Co-supervisors Dr Johan Oosthuizen (Stellenbosch University) Dr Marjo van Koppen (University of Utrecht) December 2017 This dissertation was completed as part of a Joint Doctorate with the University of Utrecht. Stellenbosch University https://scholar.sun.ac.za DECLARATION By submitting this dissertation electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the owner of the copyright thereof (unless to the extent explicitly otherwise stated) and that I have not previously in its entirety or in part submitted it for obtaining any qualification. December 2017 Date The financial assistance of the South African National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF. The author also wishes to acknowledge the financial assistance provided by EUROSA of the European Union, and the Foundation Study Fund for South African Students of Zuid-Afrikahuis. Copyright © 2017 Stellenbosch University Stellenbosch University https://scholar.sun.ac.za For Helgard, my best friend and unconquerable companion – all my favourite spaces have you in them; thanks for sharing them with me For Hester, who made it possible for me to do what I love For Theresa, who always takes the stairs Stellenbosch University https://scholar.sun.ac.za ABSTRACT Elements of language that are typically considered to have P (i.e.
    [Show full text]
  • Techniques and Challenges in Speech Synthesis Final Report for ELEC4840B
    Techniques and Challenges in Speech Synthesis Final Report for ELEC4840B David Ferris - 3109837 04/11/2016 A thesis submitted in partial fulfilment of the requirements for the degree of Bachelor of Engineering in Electrical Engineering at The University of Newcastle, Australia. Abstract The aim of this project was to develop and implement an English language Text-to-Speech synthesis system. This first involved an extensive study of the mechanisms of human speech production, a review of modern techniques in speech synthesis, and analysis of tests used to evaluate the effectiveness of synthesized speech. It was determined that a diphone synthesis system was the most effective choice for the scope of this project. A diphone synthesis system operates by concatenating sections of recorded human speech, with each section containing exactly one phonetic transition. By using a database that contains recordings of all possible phonetic transitions within a language, or diphones, a diphone synthesis system can produce any word by concatenating the correct diphone sequence. A method of automatically identifying and extracting diphones from prompted speech was designed, allowing for the creation of a diphone database by a speaker in less than 40 minutes. The Carnegie Mellon University Pronouncing Dictionary, or CMUdict, was used to determine the pronunciation of known words. A system for smoothing the transitions between diphone recordings was designed and implemented. CMUdict was then used to train a maximum-likelihood prediction system to determine the correct pronunciation of unknown English language alphabetic words. Using this, the system was able to find an identical or reasonably similar pronunciation for over 76% of the training set.
    [Show full text]
  • The Computer Generation of Cryptic Crossword Clues
    Riddle posed by computer (6) : The Computer Generation of Cryptic Crossword Clues David Hardcastle Birkbeck, London University 2007 Thesis submitted in fulfilment of requirements for degree of PhD The copyright of this thesis rests with the author. Due acknowledgement must always be made of any quotation or information derived from it. 1 Abstract This thesis describes the development of a system (ENIGMA) that generates a wide variety of cryptic crossword clues for any given word. A valid cryptic clue must appear to make sense as a fragment of natural language, but it must also be possible for the reader to interpret it using the syntax and semantics of crossword convention and solve the puzzle that it sets. It should also have a fluent surface reading that conflicts with the crossword clue interpretation of the same text. Consider, for example, the following clue for the word THESIS: Article with spies’ view (6) This clue sets a puzzle in which the (the definite article) must be combined with SIS (Special Intelligence Service) to create the word thesis (a view), but the reader is distracted by the apparent meaning and structure of the natural language fragment when attempting to solve it. I introduce the term Natural Language Creation (NLC) to describe the process through which ENIGMA generates a text with two layers of meaning. It starts with a representation of the puzzle meaning of the clue, and generates both a layered text that communicates this puzzle and a fluent surface meaning for that same text with a shallow, lexically-bound semantic representation. This parallel generation process reflects my intuition of the creative process through which cryptic clues are written, and domain expert commentary on clue-setting.
    [Show full text]
  • Automatic Lexico-Semantic Acquisition for Question Answering Plas, Marie Louise Elizabeth Van Der
    University of Groningen Automatic lexico-semantic acquisition for question answering Plas, Marie Louise Elizabeth van der IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2008 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Plas, M. L. E. V. D. (2008). Automatic lexico-semantic acquisition for question answering. s.n. Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date: 24-09-2021 Automatic Lexico-Semantic Acquisition for Question Answering Lonneke van der Plas ii This research was carried out in the project Question Answering using Dependency Relations, which is part of the research programme for Interactive Multimedia Information eXtraction, IMIX, financed by NWO, the Netherlands organisation for scientific research. The work in this thesis has been carried out under the aus- pices of the LOT school and the Center for Language and Cognition Groningen (CLCG) from the University of Groningen.
    [Show full text]
  • Similative Plurals and the Nature of Alternatives
    SIMILATIVE PLURALS AND THE NATURE OF ALTERNATIVES by Ryan Walter Smith Copyright c Ryan Walter Smith 2020 A Dissertation Submitted to the Faculty of the DEPARTMENT OF LINGUISTICS In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY In the Graduate College THE UNIVERSITY OF ARIZONA 2020 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the disser- tation prepared by Ryan Walter Smith, titled Similative plurals and the nature of alternatives and recommend that it be accepted as fulfilling the dissertation require- ment for the Degree of Doctor of Philosophy. Date: May 12 2020 Robert Henderson Date: May 12 2020 Heidi Harley Date: May 12 2020 Simin Karimi Date: May 12 2020 Andrew Carnie Date: May 12 2020 Final approval and acceptance of this dissertation is contingent upon the candidate's submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. Date: May 12 2020 Dissertation Director: Robert Henderson 3 ACKNOWLEDGEMENTS I've heard it said that the acknowledgements section of the dissertation is the hardest part of the entire thing, and I can see why: it's impossible to express in only a few paragraphs just how much this six-year journey has meant to me, and how much I've grown in the process. U of A, and the city of Tucson as a whole, will always hold a special place in my heart.
    [Show full text]
  • Package 'Lexicon'
    Package ‘lexicon’ October 29, 2017 Title Lexicons for Text Analysis Version 0.4.1 Maintainer Tyler Rinker <[email protected]> Description A collection of lexical hash tables, dictionaries, and word lists. Depends R (>= 3.2.2) Imports data.table, syuzhet (>= 1.0.1) Date 2017-10-28 License MIT + file LICENSE LazyData TRUE RoxygenNote 6.0.1 BugReports https://github.com/trinker/lexicon/issues?state=open URL https://github.com/trinker/lexicon Collate 'available_data.R' 'common_names.R' 'discourse_markers_alemany.R' 'dodds_sentiment.R' 'freq_first_names.R' 'freq_last_names.R' 'function_words.R' 'grady_augmented.R' 'hash_emoticons.R' 'hash_grady_pos.R' 'hash_lemmas.R' 'hash_power.R' 'hash_sentiment_huliu.R' 'utils.R' 'hash_sentiment_jockers.R' 'hash_sentiment_nrc.R' 'hash_sentiment_sentiword.R' 'hash_strength.R' 'hash_syllable.R' 'hash_valence_shifters.R' 'key_abbreviation.R' 'key_contractions.R' 'key_grade.R' 'key_grades.R' 'key_ratings.R' 'lexicon-package.R' 'nrc_emotions.R' 'pos_action_verb.R' 'pos_adverb.R' 'pos_df_irregular_nouns.R' 'pos_df_pronouns.R' 'pos_interjections.R' 'pos_preposition.R' 'pos_unchanging_nouns.R' 'profanity_alvarez.R' 'profanity_arr_bad.R' 'profanity_banned.R' 'profanity_google.R' 'profanity_von_ahn.R' 'sw_buckley_salton.R' 'sw_dolch.R' 'sw_fry_100.R' 'sw_fry_1000.R' 'sw_fry_200.R' 'sw_fry_25.R' 'sw_onix.R' NeedsCompilation no 1 2 R topics documented: Author Tyler Rinker [aut, cre] Repository CRAN Date/Publication 2017-10-29 14:13:50 UTC R topics documented: available_data . .3 common_names . .3 discourse_markers_alemany
    [Show full text]
  • Ensemble Methods for Automatic Thesaurus Extraction
    Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, July 2002, pp. 222-229. Association for Computational Linguistics. Ensemble Methods for Automatic Thesaurus Extraction James R. Curran Institute for Communicating and Collaborative Systems University of Edinburgh 2 Buccleuch Place, Edinburgh EH8 9LW United Kingdom [email protected] Abstract Ensemble methods ameliorate learner bias by amortising individual classifier bias of over differ- Ensemble methods are state of the art ent systems. For an ensemble to be more effec- for many NLP tasks. Recent work by tive than its constituents, the individual classifiers Banko and Brill (2001) suggests that this must have better than 50% accuracy and must pro- would not necessarily be true if very large duce diverse erroneous classifications (Dietterich, training corpora were available. However, 2000). Brill and Wu (1998) call this complementary their results are limited by the simplic- disagreement complementarity. Although ensem- ity of their evaluation task and individual bles are often effective on problems with small train- classifiers. ing sets, recent work suggests this may not be true as dataset size increases. Banko and Brill (2001) found Our work explores ensemble efficacy for that for confusion set disambiguation with corpora the more complex task of automatic the- larger than 100 million words, the best individual saurus extraction on up to 300 million classifiers outperformed ensemble methods. words. We examine our conflicting results One limitation of their results is the simplicity of in terms of the constraints on, and com- the task and methods used to examine the efficacy plexity of, different contextual representa- of ensemble methods.
    [Show full text]
  • Text Simplification for Enhanced Readability
    Text Simplification for Enhanced Readability∗ Siddhartha Banerjee, Nitin Kumar and C. E. Veni Madhavan Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India Keywords: Mining Text and Semi-structured Data, Text Summarization, Text Simplification. Abstract: Our goal is to perform automatic simplification of a piece of text to enhance readability. We combine the two processes of summarization and simplification on the input text to effect improvement. We mimic the human acts of incremental learning and progressive refinement. The steps are based on: (i) phrasal units in the parse tree, yield clues (handles) on paraphrasing at a local word/phrase level for simplification, (ii) phrasal units also provide the means for extracting segments of a prototype summary, (iii) dependency lists provide the coherence structures for refining a prototypical summary. A validation and evaluation of a paraphrased text can be carried out by two methodologies: (a) standardized systems of readability, precision and recall measures, (b) human assessments. Our position is that a combined paraphrasing as above, both at lexical (word or phrase) level and a linguistic-semantic (parse tree, dependencies) level, would lead to better readability scores than either approach performed separately. 1 INTRODUCTION The remainder of the article is structured as fol- lows. We give an overview of related work on text Many knowledge-rich tasks in natural language pro- simplification in section 2. We discuss our approach cessing require the input text to be easily readable in Section 3. Experiments and results are presented in by machines. Typical tasks are question-answering, Section 4. Discussion on future work concludes the summarization and machine translation.
    [Show full text]