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WO2008116843A2 - Procédé de reconnaissance de mots dans des séquences de signes - Google Patents

Procédé de reconnaissance de mots dans des séquences de signes Download PDF

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Publication number
WO2008116843A2
WO2008116843A2 PCT/EP2008/053430 EP2008053430W WO2008116843A2 WO 2008116843 A2 WO2008116843 A2 WO 2008116843A2 EP 2008053430 W EP2008053430 W EP 2008053430W WO 2008116843 A2 WO2008116843 A2 WO 2008116843A2
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WO
WIPO (PCT)
Prior art keywords
word
grams
gram
words
list
Prior art date
Application number
PCT/EP2008/053430
Other languages
German (de)
English (en)
Other versions
WO2008116843A3 (fr
Inventor
Frank Deinzer
Original Assignee
Frank Deinzer
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Frank Deinzer filed Critical Frank Deinzer
Priority to EP08718135A priority Critical patent/EP2132656A2/fr
Publication of WO2008116843A2 publication Critical patent/WO2008116843A2/fr
Publication of WO2008116843A3 publication Critical patent/WO2008116843A3/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/274—Converting codes to words; Guess-ahead of partial word inputs

Definitions

  • the invention relates to a method for word recognition in sequences of N characters, one or more of which may be ambiguous.
  • the electronic recording of texts and speech is used, for example, when entering text in keyboards.
  • the most efficient and most common is the normal computer keyboard, which provides a key for each letter key or for each character to be entered, or defines a Tastenkombina ⁇ tion.
  • Other keyboards have fewer keys, such as cell phones for sending text messages or PDAs for appointment input, special keyboards such as QWERTY keyboards, keyboards for the disabled or keyboards from Special equipment.
  • QWERTY keyboards special keyboards
  • keyboards for the disabled or keyboards from Special equipment When entering text into such keyboards, it is necessary to multiple assignments of the keys, so that the keys usually have to be pressed several times to activate the desired letter (Mulitap method).
  • 0 (6) ⁇ m, n, o, ö ⁇
  • 0 (9) ⁇ w, x, y, z ⁇
  • n-grams ie of contiguous character sequences with n characters.
  • n-grams have been used in the analysis of large amounts of data on specific contexts (or phrases), for example, by the intelligence service, such as the search of emails on selected topics etc .. They are also used for sentence recognition due to predetermined word sequences, the n- Gramme in this context.
  • character sequences also: strings
  • n-grams which can have different lengths.
  • n-grams A combination of different lengths of n-grams has proven useful where the shorter n-grams provide alternative predictions and the longer n-grams provide greater unambiguity, but have a high memory requirement, so that n> 6 does not occur in practice. Due to the different length of the n-grams, the frequencies of the individual letters, bigrams, trigrams and also short words are taken into account. The disadvantage of using the n-gram method is that the documents are only very short. Great attention has been given to the publication "Probalistic Character Disambiguation for Reduced Keyboards Using Small Text Samples" published in 1992 by JL Arnott and MY Javed, AAC Augmentative and Alternative Communication, Vol. 8, pages 215 to 223.
  • US 7 129 932 Bl deals with keyboards comprising keys with multiple occupancy, for example for PDAs etc.
  • words and the frequency of occurrence of these words are stored in a language model used.
  • ⁇ N For words that are not yet completely typed ( ⁇ N), the most probable words are proposed using the existing word or the word from the typed characters using the database, see the example "completed", "complexes" (column 4, lines 25 ff.) -
  • the character set of a just been surrounded word with the words is compared in the lexicon and suggested the most likely word in the lexicon, that is the word that most commonly chosen Language model occurs and at the same time derivable from the entered string.
  • the US 2002/0183100 Al discloses a method of Letter B ⁇ benaus inches when entering example of SMS messages.
  • a character which statistically has the highest probability as a secondary character is respectively displayed as a sequence character depending on the preceding input, ie the already entered character sequence.
  • the already entered string is fixed and will not be varied.
  • the following character is calculated using a statistical database. Dictionaries are used for character selection, of which one contains word beginnings and words with up to three characters, the other words with four characters or more. Methods of this type are referred to as prefix-based disambiguation.
  • WO 2004/003953 A1 eZiText method of Zi Corporation of Canada, Inc.
  • the frequencies of bi- and trigrams are used.
  • the prediction is based on a user dictionary, which preferably contains whole words and their frequency.
  • a disambiguation method is known in which a memory with words and also n-gram objects as well as their frequencies is used.
  • the n-gram objects can be words or parts of words and include mono-, bi- and trigrams.
  • the invention has for its object to provide a method for word recognition in character sequences, which is suitable for use in character ambiguities and in which the word recognition is carried out quickly.
  • a memory contains n-grams (strings of length n) and frequency values associated with the strings, the frequency value of an n-gram being the total number of all n-grams in a speech sample used for word recognition.
  • the display displays selected n-grams and / or recognized words, with the processor device connected to the memory and the display. From a considered sequence of characters, a list L of all n-gram combinations with N characters is created, which can be formed from the N-character sequence, taking into account the ambiguities of the individual characters contained therein.
  • a significant advantage of the method according to the invention is that, regardless of the language and key assignment used, it solves assignment problems of character strings and sequences, resulting in meaningful word hypotheses. This is because no words but n-grams are used to perform word recognition in strings.
  • the n-grams which are word parts or fragments, are used to make probable words. For this purpose, the n-grams with word probability zero are eliminated and those due to the remaining n-grams, i. the most likely word components representing possible words are displayed in list L.
  • word recognition is extremely flexible. Suggestions may be made of words that are not included in the lexicon (based on the language sample), e.g. Flowers hedgehog.
  • n 2 to 5 f (V2 to V5).
  • the list of probable Worthypo ⁇ thesen is generated after each keystroke in the input of a word, so that takes place with the tapping step continuous continuous updating of the hypotheses. From this list, if it contains more than one word hypothesis, the user can choose his correct word, if he has already typed the word completely. The way in which the selection is realized is arbitrary. If the word is not completely typed, the user will continue to type new characters.
  • the recognition method according to the invention can be applied to any languages, legal, technical areas, etc., by integrating the respective vocabulary into the statistics. Also the assignment of letters or others Characters for the keys, ie the output alphabets or key assignments, are freely selectable without requiring any changes or adaptations of the method. Already used language samples can be taken over unchanged, ie a language sample once created can be transferred without any effort to devices with other key arrangements or assignments. The adaptation to any languages with their individual characters such as the accent in French, Hebrew, Cyrillic, Greek etc. signs can be easily used. The counting of a complete language sample takes only a few minutes.
  • the method according to the invention is able to isolate, among competing characters (letters due to keystrokes or phonemes due to speech input or digital data sets) and the resulting ambiguities, possible words which may be a valid word. With every new typed or spoken letter, the possible recognized single letters are permuted, and each added letter can then be replaced by other ambiguities, which are resolved.
  • word ambiguities may exist in the word strings, with valid resolutions resulting when all resulting words are either valid whole words or valid words and at the same time have a valid word start or word end. This is illustrated by the following example, which uses the following labels:
  • a keyboard which comprises keys which are assigned to a plurality of characters and which is connected to the processor device.
  • a word ⁇ recognition method is accordingly used, which operates according to the invention.
  • a voice recording device When the process of the invention for the speech input ver ⁇ applies is, a voice recording device is used, and when the voice input phonemes or phoneme sequences is carried out conversion into N-symbol-sequences, in particular of text characters. On the N-character sequences, a word recognition method is used which operates according to the invention.
  • the method according to the invention can also be advantageously used when reading, for example, digitally present text documents with character sequences.
  • a reading unit is used for detecting the N-character sequences
  • a word recognition method is used in reading the N-character sequences.
  • the words from the speech ⁇ sample determined whose length corresponds to the n-gram length, and wherein the display of the remaining n-gram combinations of the list L are first all words sorted by the full-word Probability pG GN / NG is indicated, where GN is the whole word n-gram frequency and NG is the total number of all word n-grams of the speech sample.
  • the whole-word n-grams are mostly short words that act like a lexicon for short words taking into account the frequency of occurrence, and a meaningful sorting of word hypotheses for short words of goodness (such as "the", "marriage”, "oath") support.
  • the words "tree house”, “hello", "you", “der”, the bigram "you”, trigram "the” and the five-gram "hello” and as whole word n-grams result
  • the total numbers NG (n) of all integer n-grams are calculated. These result from the sum of all frequencies of the whole-word n-grams of the respective length.
  • the n-grams which form the beginning of a word, are determined as word-beginning n-grams.
  • A5 (tree) 1
  • A5 (hello) 1
  • Word-end n-grams are also preferably used, the word-n-grams being the n-grams that form the end of a word.
  • the word-end probability pE ⁇ En / NE is determined, where En is the word end n-gram frequency and NE is the total number of all word end n-grams of the speech sample.
  • E5 (mhaus) 1
  • E5 (hello) 1
  • the memory may store a list of characters or character sequences and their associated replacement characters, exchange character sequences, or replacement n-grams.
  • certain characters or words eg "sparrow” and not “rick”
  • certain short forms English: “dont”->"donot", French: “cest" >"c'est”
  • special characters eg smiley
  • the short forms must then also be entered in their short form in the language sample with. It may also be expedient to supplement the n-grams in the memory in order to enable the recognition of new words or special entries. The input of unknown words is not necessary.
  • Word-end n-grams convey the statement that it is a valid complete word, and other features may recognize a word as such in terms of the acquired speech data.
  • Word boundaries in particular word ends, are additionally entered to separate the word string into individual complete words, e.g. "Baumhaus” also in “Baume Haus", to share.
  • the method according to the invention can also be equipped with a word prediction. This may be done so that, based on an input N-character sequence, word recognition is performed for a character sequence having an assumed length of N + (1 to 1) characters, where 1 is the prediction length, ie the number of predicted input steps.
  • a further list L 'containing all the n-gram combinations of the list L is created therefrom, these n-gram combinations being n-grams or n-gram combinations having the length 1 to 1 are supplemented.
  • the sorting then takes place after the start word n-gram and the end word n-gram probability after pA • pW • pE.
  • the language statistics contained in the various n-gram groups is used to to shut one hand word hypotheses from ⁇ that are no words for the current language is most likely, and to the other to bring the remaining hypotheses in an order according to their probable correctness.
  • w wlw2w3.
  • .wN is a word w of length N, composed of the letters wlw2w3.
  • .WN The following occurrence probabilities are determined:
  • Word probability of the word w are calculated for the total of all trained n-gram lengths:
  • p3W (tree) W3 (construction) / NW (3) • W3 (aum) / NW (3) • W3 (umh) / NW (3) • W3 (mha) / NW (3) • W3 (hau) / NW (3) • W3 (off) / NW (3)
  • pW (tree house) ... • p2W (tree house) • p3W (tree house) • ...
  • word end probabilities Another great help in assessing whether there is a word w is, as mentioned, the word end probabilities. If there are no words in the language sample that end in the same string of letters as the word w, then this is probably not a word of the language.
  • the word end probabilities can be calculated directly from the word end n-grams:
  • An unknown end of the word does not necessarily indicate a meaningful word hypothesis, but may as well be an indication that a word is not yet fully entered.
  • FIG. 1 shows a processor device for carrying out the invention method according to the invention when entering text into a keyboard
  • FIG. 2 shows a processor device for carrying out the method according to the invention in voice recording
  • FIG. 3 shows a flow diagram of the method according to the invention for word recognition
  • Fig. 4 is a flowchart for supplementing the n-grams in the memory
  • Fig. 5 is a flow chart for predicting words in already input partial words.
  • Fig. 1 shows a processor device including peripherals, with which the inventive method can be used in the text input.
  • a keyboard 10 with keys 11, a display 13 and a memory 15 are connected to a processor device 12.
  • the keys 11 of the keyboard 10 are associated with several characters, so that in the character input not immediately unique identifiable strings, words, etc. arise.
  • the memory 15 contains n-grams and frequency values assigned to these n-grams, which are symbolized by the reference symbol 16.
  • the screen 14 of the display 13 illustrates the remaining words determined using the stored n-grams and their frequency values as possible words, here the three alternative words "the", “marriage”, "eid".
  • FIG. 2 shows a processor device with peripherals for word recognition during voice recording.
  • a Senauf ⁇ acquisition device such as a microphone 20, a display 13 and a memory 15 are connected to a processor device 21st Voice input not immediately clearly identi fiable ⁇ phonemes or derivable therefrom grapheme, N-character created Sequences of strings or words, etc. Basically, the approach is analogous to that in text input.
  • the memory 15 contains n-grams and frequency values assigned to these n-grams, which are symbolized by the reference symbol 16.
  • the screen 14 of the display 13 illustrates the remaining words determined using the stored n-grams and their frequency values as possible words, here the three alternative words "the", “marriage”, "eid”.
  • FIG. 3 shows that the method for word recognition is essentially characterized by the following method steps.
  • the method has the current status of the input, for example a sequence of N key presses available. From this input, the list L of all possible word hypotheses is generated in step 102 on the basis of the existing input ambiguities by permutation of all possible combinations.
  • the whole-word true ⁇ probabilities p G, the word probabilities pW that Wortend probabilities pE and the word initial probabilities pA are calculated for each word hypothesis list L. Based on these probabilities, in method step 104 all word hypotheses are removed from the list L whose word probabilities pW or word-start probabilities pA are zero and which therefore do not represent a valid word with great certainty.
  • a word ⁇ prediction branches off the query 112 to generate the creation of the predictive list L illustrated and explained in greater detail below in FIG. 5 '. If valid whole words exist in the list L, characterized by hypotheses with non-zero integer probabilities, the query 105 branches to the process step 106, which displays all valid whole words, descending sorted by their whole word probabilities, on the screen 14. Method step 107 removes all the hypotheses displayed in method step 106 from the list L and thus avoids the multiple output of one and the same hypothesis.
  • step 109 sorts all valid complete words, descending by product of their word-end probabilities, word-start probabilities and word probabilities, attached to the previous edition on the screen 14.
  • Method step 110 removes all the hypotheses displayed in method step 109 from the list L and thus avoids the multiple output of one and the same hypothesis. All remaining hypotheses of the list L are added to the previous output on the screen 14 in step 111, sorted in descending order of the product of their word-start probabilities and word probabilities. If a word prediction is to be performed, query 112 branches to output list L ', added to the previous output on screen 14.
  • query 115 branches to step 116 which selects the selected word provides any application and deletes the current character or input sequence, so that in the next input, the method of FIG. 3 in step 101 begins with an empty character sequence, ie a new word.
  • method step 201 the determination of all n-grams Vn (w) of the word w to be integrated into the memory 15 is carried out as the basis of the supplement. If the word w has a length covered by the whole-word n-grams, the query 202 branches to the process step 203 which updates the frequency of the whole-word n-gram associated with the word w.
  • the word w is integrated into the data base of the word-beginning n-grams in the memory 15 by the frequencies of the word-beginning n-grams of all n-grams Vn (w) representing valid word-beginning n-grams of the word w.
  • the word w is integrated into the database of word n-grams in memory 15 by updating the frequencies of the word n-grams of all n-grams Vn (w).
  • the word w is integrated into the database of word-end n-grams in the memory 15 by updating the frequencies of the word-end n-grams of all n-grams Vn (w), the valid word-end n-grams of the word represent w.
  • FIG. 5 describes the steps for generating a word prediction list L ', referenced in FIG. 3, method ⁇ step 114. From a list L in process step 301 a new list L' generated which for each hypothesis from the list L all concatenation this hypothesis with all permutations of the known output alphabet in the lengths of 1 to 1 characters. From the list L ', in the step 302, all hypotheses are removed which have either a word probability of zero or a word start probability of zero or a word end probability of zero.
  • the remaining hypotheses of the list L ' are sorted in step 303 so that all hypotheses that represent a valid whole-word and are decreasing by the whole ⁇ word probability pG, followed by the other Hypo ⁇ theses and these in descending order according to the product of their Word end probabilities, beginning of word probabilities and word probabilities are sorted.
  • the output 304 of the prediction method is thus the sorted list L '.
  • Table 2 shows the result, with the searched or typed word shown in bold. Behind it stands the respective word hypothesis list,
  • Table 2 The differences between the two word recognition methods are essentially not in the processing of simple, common words. Many compound words that can be obtained using the method according to the invention can not be found with the conventional T9 method. The calculation time for creating the above-mentioned word hypothesis list is in the non-measurable range.
  • 0 (0) ⁇ a, b, c, d ⁇
  • 0 (1) ⁇ e, f, g, h ⁇
  • 0 (2) ⁇ i, l, m, n ⁇

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Machine Translation (AREA)
  • Character Discrimination (AREA)

Abstract

L'invention concerne un procédé de reconnaissance de mots dans des séquences de N signes, dont un ou plusieurs signes peuvent être interprétés de manière équivoque, procédé dans lequel on utilise une mémoire (15), un afficheur (13) et un dispositif processeur (12). La mémoire renferme n-gramme (chaînes de signes de longueurs n) et des valeurs de fréquence associées aux chaînes de signes, cependant qu'on utilise comme valeur de fréquence d'un n-gramme, le nombre total de tous les n-gramme dans un échantillon de pointes vocales utilisées pour la reconnaissance de mots. L'afficheur (13) indique les n-gramme sélectionnés et/ou des mots reconnus, le dispositif processeur (12) étant associé avec la mémoire (15) et l'afficheur (13). A partir d'une séquence de signes considérée, un établit une liste L de tous les n-gramme à N signes qui peuvent être formés à partir de la séquence à N signes, en tenant compte des ambiguïtés des signes individuels contenus dans cette liste. A partir de la liste L des combinaisons n-gramme possibles, on élimine toutes les combinaisons n-gramme dont la probabilité des mots est nulle, la probabilité de mots p = Π pn étant déterminée à partir des n-gramme contenus dans la séquence de signes, avec n = 1 jusqu'à N-1. Les mots (14) de la liste L représentés par les combinaisons n-gramme restantes sont indiqués par l'afficheur.
PCT/EP2008/053430 2007-03-26 2008-03-20 Procédé de reconnaissance de mots dans des séquences de signes WO2008116843A2 (fr)

Priority Applications (1)

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EP08718135A EP2132656A2 (fr) 2007-03-26 2008-03-20 Procédé de reconnaissance de mots dans des séquences de signes

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102007014405A DE102007014405B4 (de) 2007-03-26 2007-03-26 Verfahren zur Worterkennung in Zeichensequenzen
DE102007014405.0 2007-03-26

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WO2008116843A2 true WO2008116843A2 (fr) 2008-10-02
WO2008116843A3 WO2008116843A3 (fr) 2009-01-29

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Cited By (6)

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Publication number Priority date Publication date Assignee Title
US8474755B2 (en) 2008-11-20 2013-07-02 Airbus Operations Gmbh Supply unit for flexible supply channels
US9046932B2 (en) 2009-10-09 2015-06-02 Touchtype Ltd System and method for inputting text into electronic devices based on text and text category predictions
US9189472B2 (en) 2009-03-30 2015-11-17 Touchtype Limited System and method for inputting text into small screen devices
US9424246B2 (en) 2009-03-30 2016-08-23 Touchtype Ltd. System and method for inputting text into electronic devices
US9659002B2 (en) 2009-03-30 2017-05-23 Touchtype Ltd System and method for inputting text into electronic devices
US10372310B2 (en) 2016-06-23 2019-08-06 Microsoft Technology Licensing, Llc Suppression of input images

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Publication number Priority date Publication date Assignee Title
US5031206A (en) * 1987-11-30 1991-07-09 Fon-Ex, Inc. Method and apparatus for identifying words entered on DTMF pushbuttons
ATE191282T1 (de) 1995-07-26 2000-04-15 Tegic Communications Inc System zur unterdrückung der vieldeutigkeit in einer verringerten tastatur
US5952942A (en) * 1996-11-21 1999-09-14 Motorola, Inc. Method and device for input of text messages from a keypad
FI974576L (fi) 1997-12-19 1999-06-20 Nokia Mobile Phones Ltd Menetelmä tekstin kirjoittamiseksi matkaviestimeen ja matkaviestin
GB2373907B (en) 2001-03-29 2005-04-06 Nec Technologies Predictive text algorithm
US6794966B2 (en) 2002-07-01 2004-09-21 Tyco Electronics Corporation Low noise relay
US7129932B1 (en) * 2003-03-26 2006-10-31 At&T Corp. Keyboard for interacting on small devices
EP1710668A1 (fr) 2005-04-04 2006-10-11 Research In Motion Limited Appareil électronique portatif pour la désambiguïsation de texte à l'aide d'une fonction avancée d'édition

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8474755B2 (en) 2008-11-20 2013-07-02 Airbus Operations Gmbh Supply unit for flexible supply channels
US9189472B2 (en) 2009-03-30 2015-11-17 Touchtype Limited System and method for inputting text into small screen devices
US9424246B2 (en) 2009-03-30 2016-08-23 Touchtype Ltd. System and method for inputting text into electronic devices
US9659002B2 (en) 2009-03-30 2017-05-23 Touchtype Ltd System and method for inputting text into electronic devices
US10073829B2 (en) 2009-03-30 2018-09-11 Touchtype Limited System and method for inputting text into electronic devices
US10402493B2 (en) 2009-03-30 2019-09-03 Touchtype Ltd System and method for inputting text into electronic devices
US9046932B2 (en) 2009-10-09 2015-06-02 Touchtype Ltd System and method for inputting text into electronic devices based on text and text category predictions
US10372310B2 (en) 2016-06-23 2019-08-06 Microsoft Technology Licensing, Llc Suppression of input images

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Publication number Publication date
DE102007014405A1 (de) 2008-10-09
WO2008116843A3 (fr) 2009-01-29
EP2132656A2 (fr) 2009-12-16
DE102007014405B4 (de) 2010-05-27

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