Lexical Analyser for Syllable Matching Used in Speech Synthesis

Total Page:16

File Type:pdf, Size:1020Kb

Lexical Analyser for Syllable Matching Used in Speech Synthesis Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation, Corfu Island, Greece, February 16-19, 2007 77 A Romanian Syllable-Based Text-To-Speech System OVIDIU BUZA, GAVRIL TODEREAN Department of Telecommunications Technical University of Cluj-Napoca 26 – 28 G. Baritiu Str., 400027, Cluj-Napoca ROMANIA Abstract: - In this article we present the way we have built a syllable-based TTS system for Romanian. The system contains: a text analyser capable to separate syllables from input text and detect accentuation, a vocal database with recorded syllables, a unit matching module and a synthesizer. The analyser was built using a LEX generator by mean of two sets of phonetic rules. Vocal database was generated through an automated wave segmentation procedure. Key-Words: - syllable text-to-speech system, rule-driven text analyser, automatic segmentation 1 Introduction Also, Romanian language contains a relative Concatenation of waveforms represents a method small number of syllables, so we have obtained a more and more used in our days because of high reduced size of vocal database. level of naturalness in produced speech. Corpus- Our text-to-speech system consists of ([7]): based methods are among best approaches, but - a text analysis module that brings input text and they need great efforts for database maintaining. produces basic units, that in our approach are Syllable-base methods can be an alternative, as syllables, and prosody data, which mean the they need a limited units database. Using of information about how words are accentuated; syllables in synthesis also leads to a good level of - a unit matching module that generates acoustic speech naturalness and low concatenation error units sequence according to the linguistic units rate because of small number of concatenation detected from the input and prosody data; points inside the synthesized text. - a speech synthesis module that generates speech This article presents an original approach for based on the acoustic units sequences. constructing a syllable-based TTS system for Romanian. The syllable approach is very The particular aspects of our work are: appropriate in our case, because Romanian spoken - using of linguistic and phonetic rules based of language contains a big number of opened vowels which we have done text analysis and obtained that gives a constant rhythm of speech and similar appropriate units and prosody data; manner of accentuating words. - automatic generation of database from recorded sequences. Basic units Text Unit Text Synthesis Analysis Matching Prosody data Fig.1. Main functionalities of our Text-to-Speech system Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation, Corfu Island, Greece, February 16-19, 2007 78 First of all we have built a linguistic analyzer detection of syllables we had to design a set of module that is capable to split the input text into linguistic rules for splitting words into syllables, syllables. Next step was to determine accentuation inspired from Romanian syntax rules ([2], [3]). by mean of a phonetic analyzer. Then we have The principle used in detecting linguistic units automatically produced a database with PCM is illustrated in figure no. 2. Here we can see the coded syllables of Romanian language. Synthesis structure of text analyser that corresponds to four was done by concatenating acoustic units from modules designed for detection of units, prosody database and giving appropriate commands to the information and unit processing. computer’ sound blaster for voice generation. These modules are: - lexical analysis module for detection of basic units; 2 Text Analysis - phonetic analysis module for generating prosody First stage in text analysis is the detection of information; linguistic units: sentences, words and segmental - high level analysis module for detection of high- units, that in our approach are the word syllables. level units; Detection of sentences and words is done - processing shell for unit processing. based on punctuation and literal separators. For Sub Unit Processing Routines processing Shell Sintactical Sentences High Level Rules Analysis Words Text Lexical Phonetic Stress Analysis Syllables Analysis position Numbers Separators Lexical Phonetic Rules Rules Fig.2. Text analyser for syllable detection Processing shell accomplishes the unit characters, special characters and punctuation processing task and controls the subsequent marks. Using special lexical rules (that have been modules. The shell calls high-level analyser for presented in [8]), alphabetical characters are returning main syntactic units. High-level clustered as syllables, digits are clustered as analyser calls the lexical analyser for input text numbers and special characters and punctuation parsing and detection of basic units. Then marks are used in determining of word and phonetic analysis module is called for generating sentence boundaries. stress information. Phonetic analyser gets the syllables between Lexical analyser extracts text characters and two breaking characters and detects stress clusters them into basic units. We refer to the position, i.e. the accentuated syllable from detection of alphabetical characters, numerical corresponding word. Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation, Corfu Island, Greece, February 16-19, 2007 79 Then, high-level analyser takes the syllables, Text special characters and numbers provided by the C C C C C C C C C C lexical analyser, and also prosodic information, and constructs high-level units: words and sentences. Also basic sentence verification is done Digit Separator Alphanumeric here. Processing shell finally takes linguistic units provided from the previous levels and, based on some computing subroutines, classifies and stores Rules Rules Rules them in appropriate structures. From here synthesis module will construct the acoustic waves and will synthesize the text. Integer Real Sep 1 … Sep n Syllable 3 Lexical Analysis for Syllable Process separator Detection Process number Process syllable Lexical analyzer is called by the higher level modules for detection of basic lexical units: syllables, breaking characters and numbers. The Fig.3. Lexical analyser for syllable detection lexical analyzer is made by using LEX scanner generator [4]. LEX generates a lexical scanner starting from an input grammar that describes the 3.1 Specific actions of lexical analyser parsing rules. Grammar is written in BNF Specific actions inform high-level module about standard form and specifies character sequences matching of syllables, numbers and breaking that can be recognized from the input. These characters. Inside lexical parser three types of sequences refer to syllables, special characters, input response actions are defined as follows: separators and numbers. Also BNF grammar A. Process syllable – this is the action to be specifies the actions to be taken in the response of taken when a syllable is matched in specific input matching, actions that will be accomplished location of one word. by the processing shell subroutines. Special attention is taken when a syllable is The whole process realized by the lexical matched at the beginning of a word. In Romanian, analyzer is illustrated in figure no. 3. As we can different word decomposition rules apply when a see, input text is interpreted as a character string. character sequence occurs at the beginning or in At the beginning, current character is classified in the middle or the final part of a word. following categories: digit, special character or B. Process number – is the action to be separator, and alphanumeric character. Taking taken when a number is matched from the input. into account left and right context, current The number is identified as INTEGER or REAL character and the characters already parsed are type. In future stage, numbers will be translated in grouped to form a lexical unit: a syllable, a orthographic alphabetical form. number or a separator. Specific production rules C. Process separator - is the action for each category indicate the mode each lexical corresponding to a breaking character matching unit is formed and classified, and also realize a from the input. Breaking characters and subclasification of units (for numbers if they are punctuation marks are used for detecting word integer or real numbers, and for separators – the and sentence boundaries. type: word or sentence separator, affirmative, interrogative, imperative or special separator). 3.2 Syllable rules matching Once the unit type and subtype is identified, Regarding syllable rules matching process inside corresponding character sequence is stored and lexical analyser, two types of rule sets were made: transmitted to the high-level analyzer by mean of a basic set consisting of three general rules, and a specific actions, as they will be described in next large set of exception rules which statues the paragraph (Process syllable, Process number, exceptions from the basic set. Process separator). Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation, Corfu Island, Greece, February 16-19, 2007 80 (A) The basic set shows the general In Romanian stressed syllable can be one of decomposition rules for Romanian. First rule is last four syllables of the word: Sn, Sn-1, Sn-2 or Sn-3, that a syllable consists of a sequence of ( Sn is the last syllable). Most often, stress is
Recommended publications
  • A Way with Words: Recent Advances in Lexical Theory and Analysis: a Festschrift for Patrick Hanks
    A Way with Words: Recent Advances in Lexical Theory and Analysis: A Festschrift for Patrick Hanks Gilles-Maurice de Schryver (editor) (Ghent University and University of the Western Cape) Kampala: Menha Publishers, 2010, vii+375 pp; ISBN 978-9970-10-101-6, e59.95 Reviewed by Paul Cook University of Toronto In his introduction to this collection of articles dedicated to Patrick Hanks, de Schryver presents a quote from Atkins referring to Hanks as “the ideal lexicographer’s lexicogra- pher.” Indeed, Hanks has had a formidable career in lexicography, including playing an editorial role in the production of four major English dictionaries. But Hanks’s achievements reach far beyond lexicography; in particular, Hanks has made numerous contributions to computational linguistics. Hanks is co-author of the tremendously influential paper “Word association norms, mutual information, and lexicography” (Church and Hanks 1989) and maintains close ties to our field. Furthermore, Hanks has advanced the understanding of the relationship between words and their meanings in text through his theory of norms and exploitations (Hanks forthcoming). The range of Hanks’s interests is reflected in the authors and topics of the articles in this Festschrift; this review concentrates more on those articles that are likely to be of most interest to computational linguists, and does not discuss some articles that appeal primarily to a lexicographical audience. Following the introduction to Hanks and his career by de Schryver, the collection is split into three parts: Theoretical Aspects and Background, Computing Lexical Relations, and Lexical Analysis and Dictionary Writing. 1. Part I: Theoretical Aspects and Background Part I begins with an unfinished article by the late John Sinclair, in which he begins to put forward an argument that multi-word units of meaning should be given the same status in dictionaries as ordinary headwords.
    [Show full text]
  • Finetuning Pre-Trained Language Models for Sentiment Classification of COVID19 Tweets
    Technological University Dublin ARROW@TU Dublin Dissertations School of Computer Sciences 2020 Finetuning Pre-Trained Language Models for Sentiment Classification of COVID19 Tweets Arjun Dussa Technological University Dublin Follow this and additional works at: https://arrow.tudublin.ie/scschcomdis Part of the Computer Engineering Commons Recommended Citation Dussa, A. (2020) Finetuning Pre-trained language models for sentiment classification of COVID19 tweets,Dissertation, Technological University Dublin. doi:10.21427/fhx8-vk25 This Dissertation is brought to you for free and open access by the School of Computer Sciences at ARROW@TU Dublin. It has been accepted for inclusion in Dissertations by an authorized administrator of ARROW@TU Dublin. For more information, please contact [email protected], [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License Finetuning Pre-trained language models for sentiment classification of COVID19 tweets Arjun Dussa A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computer Science (Data Analytics) September 2020 Declaration I certify that this dissertation which I now submit for examination for the award of MSc in Computing (Data Analytics), is entirely my own work and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the test of my work. This dissertation was prepared according to the regulations for postgraduate study of the Technological University Dublin and has not been submitted in whole or part for an award in any other Institute or University.
    [Show full text]
  • Lexical Analysis
    From words to numbers Parsing, tokenization, extract information Natural Language Processing Piotr Fulmański Lecture goals • Tokenizing your text into words and n-grams (tokens) • Compressing your token vocabulary with stemming and lemmatization • Building a vector representation of a statement Natural Language Processing in Action by Hobson Lane Cole Howard Hannes Max Hapke Manning Publications, 2019 Terminology Terminology • A phoneme is a unit of sound that distinguishes one word from another in a particular language. • In linguistics, a word of a spoken language can be defined as the smallest sequence of phonemes that can be uttered in isolation with objective or practical meaning. • The concept of "word" is usually distinguished from that of a morpheme. • Every word is composed of one or more morphemes. • A morphem is the smallest meaningful unit in a language even if it will not stand on its own. A morpheme is not necessarily the same as a word. The main difference between a morpheme and a word is that a morpheme sometimes does not stand alone, but a word, by definition, always stands alone. Terminology SEGMENTATION • Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. • Word segmentation is the problem of dividing a string of written language into its component words. Text segmentation, retrieved 2020-10-20, https://en.wikipedia.org/wiki/Text_segmentation Terminology SEGMENTATION IS NOT SO EASY • Trying to resolve the question of what a word is and how to divide up text into words we can face many "exceptions": • Is “ice cream” one word or two to you? Don’t both words have entries in your mental dictionary that are separate from the compound word “ice cream”? • What about the contraction “don’t”? Should that string of characters be split into one or two “packets of meaning?” • The single statement “Don’t!” means “Don’t you do that!” or “You, do not do that!” That’s three hidden packets of meaning for a total of five tokens you’d like your machine to know about.
    [Show full text]
  • Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph
    mathematics Article Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph Tajana Ban Kirigin 1,* , Sanda Bujaˇci´cBabi´c 1 and Benedikt Perak 2 1 Department of Mathematics, University of Rijeka, R. Matejˇci´c2, 51000 Rijeka, Croatia; [email protected] 2 Faculty of Humanities and Social Sciences, University of Rijeka, SveuˇcilišnaAvenija 4, 51000 Rijeka, Croatia; [email protected] * Correspondence: [email protected] Abstract: This paper describes a graph method for labeling word senses and identifying lexical sentiment potential by integrating the corpus-based syntactic-semantic dependency graph layer, lexical semantic and sentiment dictionaries. The method, implemented as ConGraCNet application on different languages and corpora, projects a semantic function onto a particular syntactical de- pendency layer and constructs a seed lexeme graph with collocates of high conceptual similarity. The seed lexeme graph is clustered into subgraphs that reveal the polysemous semantic nature of a lexeme in a corpus. The construction of the WordNet hypernym graph provides a set of synset labels that generalize the senses for each lexical cluster. By integrating sentiment dictionaries, we introduce graph propagation methods for sentiment analysis. Original dictionary sentiment values are integrated into ConGraCNet lexical graph to compute sentiment values of node lexemes and lexical clusters, and identify the sentiment potential of lexemes with respect to a corpus. The method can be used to resolve sparseness of sentiment dictionaries and enrich the sentiment evaluation of Citation: Ban Kirigin, T.; lexical structures in sentiment dictionaries by revealing the relative sentiment potential of polysemous Bujaˇci´cBabi´c,S.; Perak, B. Lexical Sense Labeling and Sentiment lexemes with respect to a specific corpus.
    [Show full text]
  • Automated Citation Sentiment Analysis Using High Order N-Grams: a Preliminary Investigation
    Turkish Journal of Electrical Engineering & Computer Sciences Turk J Elec Eng & Comp Sci (2018) 26: 1922 – 1932 http://journals.tubitak.gov.tr/elektrik/ © TÜBİTAK Research Article doi:10.3906/elk-1712-24 Automated citation sentiment analysis using high order n-grams: a preliminary investigation Muhammad Touseef IKRAM1;∗,, Muhammad Tanvir AFZAL1, Naveed Anwer BUTT2, 1Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan 2Department of Computer Science, University of Gujrat, Gujrat, Pakistan Received: 03.12.2017 • Accepted/Published Online: 02.02.2018 • Final Version: 27.07.2018 Abstract: Scientific papers hold an association with previous research contributions (i.e. books, journals or conference papers, and web resources) in the form of citations. Citations are deemed as a link or relatedness of the previous work to the cited work. The nature of the cited material could be supportive (positive), contrastive (negative), or objective (neutral). Extraction of the author’s sentiment towards the cited scientific articles is an emerging research discipline due to various linguistic differences between the citation sentences and other domains of sentiment analysis. In this paper, we propose a technique for the identification of the sentiment of the citing author towards the cited paper by extracting unigram, bigram, trigram, and pentagram adjective and adverb patterns from the citation text. After POS tagging of the citation text, we use the sentence parser for the extraction of linguistic features comprising adjectives, adverbs, and n-grams from the citation text. A sentiment score is then assigned to distinguish them as positive, negative, and neutral. In addition, the proposed technique is compared with manually classified citation text and 2 commercial tools, namely SEMANTRIA and THEYSAY, to determine their applicability to the citation corpus.
    [Show full text]
  • A Framework for Representing Lexical Resources
    A framework for representing lexical resources Fabrice Issac LDI Universite´ Paris 13 Abstract demonstration of the use of lexical resources in different languages. Our goal is to propose a description model for the lexicon. We describe a software 2 Context framework for representing the lexicon and its variations called Proteus. Various Whatever the writing system of a language (logo- examples show the different possibilities graphic, syllabic or alphabetic), it seems that the offered by this tool. We conclude with word is a central concept. Nonetheless, the very a demonstration of the use of lexical re- definition of a word is subject to variation depend- sources in complex, real examples. ing on the language studied. For some Asian lan- guages such as Mandarin or Vietnamese, the no- 1 Introduction tion of word delimiter does not exist ; for others, such as French or English, the space is a good in- Natural language processing relies as well on dicator. Likewise, for some languages, preposi- methods, algorithms, or formal models as on lin- tions are included in words, while for others they guistic resources. Processing textual data involves form a separate unit. a classic sequence of steps : it begins with the nor- malisation of data and ends with their lexical, syn- Languages can be classified according to their tactic and semantic analysis. Lexical resources are morphological mechanisms and their complexity ; the first to be used and must be of excellent qual- for instance, the morphological systems of French ity. or English are relatively simple compared to that Traditionally, a lexical resource is represented of Arabic or Greek.
    [Show full text]
  • Content Extraction and Lexical Analysis from Customer-Agent Interactions
    Lexical Analysis and Content Extraction from Customer-Agent Interactions Sergiu Nisioi Anca Bucur? Liviu P. Dinu Human Language Technologies Research Center, ?Center of Excellence in Image Studies, University of Bucharest fsergiu.nisioi,[email protected], [email protected] Abstract but also to forward a solution for cleaning and ex- tracting useful content from raw text. Given the In this paper, we provide a lexical compara- large amounts of unstructured data that is being tive analysis of the vocabulary used by cus- collected as email exchanges, we believe that our tomers and agents in an Enterprise Resource proposed method can be a viable solution for con- Planning (ERP) environment and a potential solution to clean the data and extract relevant tent extraction and cleanup as a preprocessing step content for NLP. As a result, we demonstrate for indexing and search, near-duplicate detection, that the actual vocabulary for the language that accurate classification by categories, user intent prevails in the ERP conversations is highly di- extraction or automatic reply generation. vergent from the standardized dictionary and We carry our analysis for Romanian (ISO 639- further different from general language usage 1 ro) - a Romance language spoken by almost as extracted from the Common Crawl cor- 24 million people, but with a relatively limited pus. Moreover, in specific business commu- nication circumstances, where it is expected number of NLP resources. The purpose of our to observe a high usage of standardized lan- approach is twofold - to provide a comparative guage, code switching and non-standard ex- analysis between how words are used in question- pression are predominant, emphasizing once answer interactions between customers and call more the discrepancy between the day-to-day center agents (at the corpus level) and language as use of language and the standardized one.
    [Show full text]
  • A Corpus-Based Investigation of Lexical Cohesion in En & It
    A CORPUS-BASED INVESTIGATION OF LEXICAL COHESION IN EN & IT NON-TRANSLATED TEXTS AND IN IT TRANSLATED TEXTS A thesis submitted To Kent State University in partial Fufillment of the requirements for the Degree of Doctor of Philosophy by Leonardo Giannossa August, 2012 © Copyright by Leonardo Giannossa 2012 All Rights Reserved ii Dissertation written by Leonardo Giannossa M.A., University of Bari – Italy, 2007 B.A., University of Bari, Italy, 2005 Approved by ______________________________, Chair, Doctoral Dissertation Committee Brian Baer ______________________________, Members, Doctoral Dissertation Committee Richard K. Washbourne ______________________________, Erik Angelone ______________________________, Patricia Dunmire ______________________________, Sarah Rilling Accepted by ______________________________, Interim Chair, Modern and Classical Language Studies Keiran J. Dunne ______________________________, Dean, College of Arts and Sciences Timothy Moerland iii Table of Contents LIST OF FIGURES .......................................................................................................... vii LIST OF TABLES ........................................................................................................... viii DEDICATION ................................................................................................................... xi ACKNOWLEDGEMENTS .............................................................................................. xii ABSTRACT ....................................................................................................................
    [Show full text]
  • Lexical Analysis
    Lexical Analysis Lecture 3 Instructor: Fredrik Kjolstad Slide design by Prof. Alex Aiken, with modifications 1 Outline • Informal sketch of lexical analysis – Identifies tokens in input string • Issues in lexical analysis – Lookahead – Ambiguities • Specifying lexers (aka. scanners) – By regular expressions (aka. regex) – Examples of regular expressions 2 Lexical Analysis • What do we want to do? Example: if (i == j) Z = 0; else Z = 1; • The input is just a string of characters: \tif (i == j)\n\t\tz = 0;\n\telse\n\t\tz = 1; • Goal: Partition input string into substrings – Where the substrings are called tokens 3 What’s a Token? • A syntactic category – In English: noun, verb, adjective, … – In a programming language: Identifier, Integer, Keyword, Whitespace, … 4 Tokens • A token class corresponds to a set of strings Infinite set • Examples i var1 ports – Identifier: strings of letters or foo Person digits, starting with a letter … – Integer: a non-empty string of digits – Keyword: “else” or “if” or “begin” or … – Whitespace: a non-empty sequence of blanks, newlines, and tabs 5 What are Tokens For? • Classify program substrings according to role • Lexical analysis produces a stream of tokens • … which is input to the parser • Parser relies on token distinctions – An identifier is treated differently than a keyword 6 Designing a Lexical Analyzer: Step 1 • Define a finite set of tokens – Tokens describe all items of interest • Identifiers, integers, keywords – Choice of tokens depends on • language • design of parser 7 Example • Recall \tif
    [Show full text]
  • Text-To-Speech Synthesis
    Sproat, R. & Olive, J. “Text-to-Speech Synthesis” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c 1999byCRCPressLLC 46 Text-to-Speech Synthesis Richard Sproat 46.1 Introduction 46.2 Text Analysis and Linguistic Analysis Bell Laboratories • • • Lucent Technologies Text Preprocessing Accentuation Word Pronunciation In- tonational Phrasing • Segmental Durations • Intonation Joseph Olive 46.3 Speech Synthesis Bell Laboratories 46.4 The Future of TTS Lucent Technologies References 46.1 Introduction Text-to-speech synthesis has had a long history, one that can be traced back at least to Dudley’s “Voder”, developed at Bell Laboratories and demonstrated at the 1939 World’s Fair [1]. Practical systems for automatically generating speech parameters from a linguistic representation (such as a phoneme string) were not available until the 1960s, and systems for converting from ordinary text into speech were first completed in the 1970s, with MITalk being the best-known such system [2]. Many projects in text-to-speech conversion have been initiated in the intervening years, and papers on many of these systems have been published.1 It is tempting to think of the problem of converting written text into speech as “speech recognition in reverse”: current speech recognition systems are generally deemed successful if they can convert speech input into the sequence of words that was uttered by the speaker, so one might imagine that a text-to-speech (TTS) synthesizer would start with the words in the text, convert each word one-by-one into speech (being careful to pronounce each word correctly), and concatenate the result together.
    [Show full text]
  • Robust Extended Tokenization Framework for Romanian by Semantic Parallel Texts Processing
    International Journal on Natural Language Computing (IJNLC) Vol. 2, No.6, December 2013 ROBUST EXTENDED TOKENIZATION FRAMEWORK FOR ROMANIAN BY SEMANTIC PARALLEL TEXTS PROCESSING Eng. Marius Zubac 1 and Prof. PhD Eng. Vasile D ădârlat 2 1Department of Computer Science, Technical University, Cluj-Napoca, Romania 2Department of Computer Science, Technical University, Cluj-Napoca, Romania ABSTRACT Tokenization is considered a solved problem when reduced to just word borders identification, punctuation and white spaces handling. Obtaining a high quality outcome from this process is essential for subsequent NLP piped processes (POS-tagging, WSD). In this paper we claim that to obtain this quality we need to use in the tokenization disambiguation process all linguistic, morphosyntactic, and semantic-level word-related information as necessary. We also claim that semantic disambiguation performs much better in a bilingual context than in a monolingual one. Then we prove that for the disambiguation purposes the bilingual text provided by high profile on-line machine translation services performs almost to the same level with human-originated parallel texts (Gold standard). Finally we claim that the tokenization algorithm incorporated in TORO can be used as a criterion for on-line machine translation services comparative quality assessment and we provide a setup for this purpose. KEYWORDS Semantic Tokenization, Bi-lingual Corpora, Romanian WordNet, Bing, Google Translate, Comparative MT Quality Assessment 1. INTRODUCTION When faced with the task of text processing today’s systems need to implement robust procedures for input text acceptance no matter the final processing purpose ranging from information retrieval and sentiment analysis to machine translation applications. This acceptance criteria may include various filtering pre-processing of the text, however the vast majority of researchers [1], [2], [3] agree that tokenization should be the first step of the text processing pipe as shown in the Figure 1 preceding any lexical analysis step.
    [Show full text]
  • Semantic Analysis Lexical Analysis
    9. Text and Document Analytics Jacobs University Visualization and Computer Graphics Lab Unstructured Data Data Mining / Knowledge Discovery Structured Data Multimedia Free Text Hypertext HomeLoan ( Frank Rizzo bought <a href>Frank Rizzo Loanee: Frank Rizzo his home from Lake </a> Bought Lender: MWF View Real Estate in <a hef>this home</a> Agency: Lake View 1992. from <a href>Lake Amount: $200,000 He paid $200,000 View Real Estate</a> Term: 15 years under a15-year loan In <b>1992</b>. ) Loans($200K,[map],...) from MW Financial. <p>... Jacobs University Visualization and Computer Graphics Lab Data Analytics 312 Unstructured Data • There are many examples of text-based documents (all in ‘electronic’ format…) – e-mails, corporate Web pages, customer surveys, résumés, medical records, DNA sequences, technical papers, incident reports, news stories, etc. • Not enough time or patience to read – Can we extract the most vital kernels of information? • Find a way to gain knowledge (in summarised form) from all that text, without reading or examining them fully first Jacobs University Visualization and Computer Graphics Lab Data Analytics 313 Tasks Jacobs University Visualization and Computer Graphics Lab Data Analytics 314 9.1 Text Mining Jacobs University Visualization and Computer Graphics Lab Text Mining Jacobs University Visualization and Computer Graphics Lab Data Analytics 316 Text Mining • Typically falls into one of two categories: – Analysis of text: I have a bunch of text I am interested in, tell me something about it • E.g. sentiment analysis, “buzz” searches –Retrieval: There is a large corpus of text documents, and I want the one closest to a specified query • E.g.
    [Show full text]