An Unsupervised Alignment Algorithm for Text Simplification Corpus
metric. The alignment algorithm is being used for the creation of a corpus for the study of text simplification in the Spanish language. 1 Introduction.
Text-Translation Alignment
We present an algorithm for aligning texts with their translations that is based only To align a text with a translation of it in another language is ...
An overview of bitext alignment algorithms
Text alignment can be done at many levels ranging from document alignment to charac- ter alignment with
Fast-Champollion: A Fast and Robust Sentence Alignment Algorithm
text and the target text. However lexicon-based algorithms are slower than length-based sentence alignment algorithms
text.alignment: Text Alignment with Smith-Waterman
The algorithm per- forms local sequence alignment and determines similar regions between two strings. The Smith-Waterman algorithm is explained in the paper: ``
Adaptive Algorithm for Plagiarism Detection: The Best-performing
Abstract. The task of (monolingual) text alignment consists in finding similar text fragments between two given documents. It has applications.
TEXT ALIGNMENT
Many modern text processors such as LATEX(which this is written on) use a sophisticated dynamic programming algorithm to assure that the lines are.
SailAlign: Robust long speech-text alignment
31 janv. 2011 SailAlign implements the adaptive iterative speech-text align- ment algorithm described as Algorithm 1 using pseudocode. As mentioned earlier
An overview of bitext alignment algorithms 1. Background
Text alignment can be done at many levels ranging from document alignment to charac- ter alignment with
Unsupervised Alignment of Actions in Video with Text Descriptions
Most algorithms for connect- ing natural language with video rely on pre-aligned supervised training data. Recently several models have been shown to be
One TTS Alignment To Rule Them All - arXivorg
Speech-to-text alignment is a critical component of neural text-to-speech (TTS) models Autoregressive TTS models typicallyuse an attention mechanism to learn these alignments on-line However these alignments tend to be brittle and often fail togeneralize to long utterances and out-of-domain text leadingto missing or repeating words
TEXT ALIGNMENT
(TBAD) of a text is the sum of the badnesses of the lines The object is to split the text into lines so as to minimize the total badnesses A natural inclination is to use a greedy algorithm: if it ?ts put it in Below is an example (the represents the end of the line) where this does not work
TEXT ALIGNMENT - New York University
Thealgorithm is given a particular badness function and a text to split into sentences 3In actual application space on the last line is not so bad as space in the middle butwe ignore that wrinkle in our presentation Now for the algorithm Set m(i) equal to the total badness of the textl1 li
Text Alignment with Handwritten Documents - UMass
First alignment algorithms al-low us to produce displays (for example on the web) that allow a person to easily ?nd their place in the manuscript when reading a transcript Second such alignment algo-rithms will allow us to produce large quantities of ground truth data for evaluating handwriting recognition algo-rithms Third such
textalignment: Text Alignment with Smith-Waterman
Align text using the Smith-Waterman algorithm The Smith–Waterman algorithm performs local sequence alignment It ?nds similar regions between two strings Similar regions are a sequence of either characters or words which are found by matching the characters or words of 2 sequences of strings If the word/letter is the same in each text
Searches related to text alignment algorithm PDF
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TEXT ALIGNMENT
Many modern text processors, such as L
ATEX(which this is written on) use a
sophisticated dynamic programming algorithm to assure that the lines are well aligned on the right side of the page. The problem is where to break the text into lines. Let usl1,l2,...,lndenote the lengths of the words of the text. Ifli,...,ljare placed on a line we assume they take up space l i+...+lj+j-i, the extra being the space between the words. (In the special case of a single wordlithe length is simplyli.) LetLdenote the than spaceLon a line, and that words can never be cut.) A line with text l i,...,ljthen has agapG=L-(li+...+lj+j-i) at the end of the line. We"d like, of course, forGto be zero but we can"t always1get this. We are given a functionP(x) and a line with gapGincurs a penalty ofP(G). For the sake of argument we will specifyP(x) =x3. We"ll say a line with gapGhasbadnessP(G) =G3.2The total badness of a text is the sum of the badnesses of the lines. The object is to split the text into lines so as to minimize the total badnesses. A natural inclination is to use agreedy algorithm: if it fits, put it in. Below is an example (the|represents the end of the line) where this does not work. The words have lengths 3,4,1,6 and the line lengthL= 10. The greedy algorithm would have 3,4,1 on the first line and 6 on the second with gaps 0,4 respectively so total badness 03+43= 64. If instead we split3,4 and 1,6 the gaps are 2,2 and the total badness is 23+ 23= 16, much
better 3NOW SING |2 8 NOW SING A|0 0
A MELODY |2 8 MELODY |4 64
total badness 16 641You might notice that the text you are reading (and most things written in LATEXor
other modern word processors) seems to be nearly perfectly aligned. But if you look very closely you"ll see that the space between letters is not perfectly uniform. When LATEXhas, say, 3 extra spaces at the end of a line it spreads the space outalong the whole line by putting extra space between letters. Exactly how it does that is itself an interesting question, but one we do not persue.2So badnesses go 0,1,8,27,64,125,.... A gap of five (badness 125) is then counted as
equivalent to nearly five gaps of three so the algorithm will really try to avoid them. The choice of badness function is a subjective decision guided by aesthetic considerations. The algorithm is given a particular badness function and a text to split into sentences.3In actual application space on the last line is not so bad as space in the middle but
we ignore that wrinkle in our presentation. Now for the algorithm. Setm(i) equal to the total badness of the text l1,...,li. We shall findm(i) fori= 1,2,...,nin increasing order.
Initialization.Supposeiis such thatl1,...,lifit on one line, i.e., everything on the same line. This gives a gapG=L-(l1+...+li+i-1) and som(i) =P(G). The recursion.Now look at largeri. We take theione by one, going up. We loop onigoing up ton. For a giveniletkrange overi,i-1,i-2,... for as long aslk,...,lican fit on one line. For each of thosekcalculate m(k-1) plus the badness of the linelk,...,li. This gives the badness of the textl1,...,liifthe last line islk,...,li. Pick thekthat gives the smallest sum and setm(i) equal to that sum. We can also keep an auxilliary array ssettings(i) =k. This has the meaning that in the optimal splitting of textl1,...,lithe last line starts withlk. At the endm(n) denotes the total badness of of the full text. To actually do the splitting we work backwards. The last line goes from words(n) to wordn. Now setn→s(n)-1 and the penultimate line goes from words(n) to wordn, etc. How long does this take. There is a loop oniof lengthn. There is an inner loop onk. Certainlyktakes on at mostnvalues so that this is aO(n2) algorithm. But in many cases we can say more. Supposeuis the maximum number of words that can fit on a line. Thenktakes on at mostuvalues so that this is aO(un) algorithm. If we think of the line size as fixed (rather a natural assumption) and the words as havinga minimal length (also natural) thenuis a fixed number and so this becomes alinear algorithm in the size of the text.quotesdbs_dbs19.pdfusesText_25[PDF] text pdf
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