Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F40/00—Handling natural language data
  • G06F40/20—Natural language analysis
  • G06F40/253—Grammatical analysis; Style critique

Definitions

• the present invention belongs to the field of automatic natural language processing. More particularly, it addresses the problem of generating a set of texts specific to a specific field of application or corpus.
• Specific corpora are necessary, in particular in computerized speech recognition systems to reach an acceptable recognition rate for the user. This is particularly necessary for systems with a large vocabulary (typically 20,000 words).
• the generation of a specific corpus is a processing which still requires, in the state of the art, long progressive learning operations based on a selection of the texts of a corpus of a given field from the sentences entered by the 'user.
• This method has the disadvantage of requiring long and costly user interactions.
• the present invention overcomes this drawback by allowing the user to formulate a specification of the specific corpus in the form of a grammar, or set of syntax rules, specific to the application domain, the generation of the specific corpus being then automatic.
• the invention thus significantly reduces the time required to collect the specific corpus.
• the invention provides a product / program and a method of collecting a set of texts specific to a field of application from a set of texts not specific, characterized in that it comprises a command module by a grammar of the field of application.
• Figure 1 Diagram showing the modules of the product / program according to the invention.
• Figure 2 Diagram showing an example of generation of n-grams of a specific grammar of an application domain.
• Figure 3 Diagram explaining an algorithm for calculating the distances between words according to the invention.
• Figure 4 Example of calculating the distances between words according to the invention.
• Figure 6 Graph showing the distribution of n-grams as a function of the distance to words of the grammar vocabulary.
• Figure 1 shows the sequence of modules and treatments according to the invention. The definitions in the figure are as follows:
• the general corpus 10 is a set of texts, commercially available, not specific to a field, which can contain several million texts.
• n-gram [V C oRPUs] 13 is a set of sequences of ordered words extracted from the general corpus or n-tuples, said words being present in the vocabulary. The manner in which these n-grams are made up is described below.
• the vocabulary of this corpus VCORPU S 1 1 is the set of words most frequently encountered in this corpus or set of monograms. Vocabulary is generally limited to 20,000 words.
• the AEF generator 20 is a module which makes it possible to generate the n-grams of a grammar of the domain ⁇ from said grammar, in a manner also explained in the following description.
• a set n- grams [Vc FG ( ⁇ )] 33 is generated from the grammar CFG ( ⁇ ) 30 in a manner explained in the following description.
• the specific corpus of ⁇ , CORPUS ( ⁇ ) 40 is initialized with the n-grams V C F G ( ⁇ ) 33.
• CORPUS ( ⁇ ) 40 we add the n-grams of VCORPUS 1 which fulfill the condition:
• ⁇ is the distance threshold which must be adjusted so as to optimize the constitution of CORPUS ( ⁇ ) 40 for specific recognition applications in the ⁇ domain.
• n-grams of VCORPUS13 will be bi-grams or tri-grams.
• a biogram is a set of two words which belong to the vocabulary V CORPUS I 1 with which are associated their probabilities of occurrence in the general corpus 10.
• Tri-grams are sets of three words in the order in which they appear in general corpus 10 with which their probabilities of occurrence are associated in general corpus 10.
• V C oRPus To generate n-grams [V C oRPus] one can use commercial tools generally designated under the generic name of tools for generating statistical language models. We can for example use the one developed by Carnegie Mellon University described by Philippe Clarkson and Ronald Rosenfeld in a University publication [Rosenfeld 95] Rosenfeld R., The CMU Statistical Language Modeling Toolkit and its use, ARPA Spoken Language Technology Workshop , Austin Texas (USA) pp 45-50, 1995. This article is incorporated by reference into the present description. Most statistical language models, and in particular the one described in the article under reference, correct the lowest occurrence probabilities so as to eliminate them. the bias which is classic in this type of statistical analysis. The least observed n-grams have a probability of occurrence biased downwards and the most observed a probability of occurrence biased upwards.
• the grammar CFG ( ⁇ ) 30 is a context-independent grammar, which means that variations in the context do not modify the grammar itself. This grammar is, in the state of the art, created manually.
• the n-grams [V C FG ( ⁇ )] 33 will typically be tri-grams or quadri-grams. They are created by the AEF generator 20, an example of which is described in FIG. 2. The generation of the n-grams of CFG ( ⁇ ) 30 takes place as follows,
• GRAMMAR unit (alpha OR bravo) (join OR (go to) unit
• V C FG (unit, alpha, bravo, join, go to, unit).
• VCGF I 6
• the uni-grams are: unit, alpha, bravo, join, go to, unity (we fall back on VCFG) - Bigrams are: alpha unit, bravo unit, alpha join, alpha go to, bravo join, bravo go to , join the unit, go to the unit, the alpha unit, the bravo unit.
• the vocabulary VCF G ( ⁇ ) 31 is the set of uni-grams.
• FIG. 3 illustrates the operation of the algorithm for calculating the distance between two words of a dictionary. In the application we use the three dictionaries 10, 12 and 32 of Figure 1.
• the dico-VcoRP U s 12 and dico-VcFG ( ⁇ ) 32 dictionaries are dictionaries extracted from a general dictionary 10a which is a commercially available component.
• This general dictionary provides information on inflected forms of words, such as pronunciation, the root of the word.
• semantic information which can be represented in the form of a graph or conceptual vectors. This algorithm has three steps:
• the editing distance returns the minimum number of editing operations necessary to transform the word a into word b.
• These editing operations are generally the insertion of a letter, the deletion of a letter and the substitution of a letter.
• D (a, b) be the function which returns the editing distance (Levenstein) which makes it possible to transform a into b.
• D ⁇ m a x be the maximum distance between any two words.
• a ⁇ VCFG ( ⁇ ) and be VCORPUS be the two words whose distance we want to measure.
• Any distance calculation function between a and b can be used. It is however preferable that the function D is continuous in pieces and increasing as a function of DQ.
• An example of the algorithm for calculating the distance between words is given below.
• Vcorpus defined by VCORPUS ⁇ , "show”, "horse” ⁇
• the shortest distance is that for the couple (display, show). It is indeed easier to insert than to delete: deletion leads to the loss of information, while insertion adds noise to the information.
• semantic calculation is made from semantic dictionaries.
• semantic dictionaries There are several forms of semantic dictionaries, two of which in particular: those based on graphs, and those based on vectors. On the example of colors, if the semantic dictionary is a graph, we can obtain the diagram of figure 5;
• the distance between colors and red is 2.
• the distance between red and green is 1.
• the distances are integer values, which makes it easier to build analysis tables that will allow you to choose the threshold.
• M (i, j) min M (i, j - 1) + D ( ⁇ , yj)
• the distance between two n-grams uses the distance of
• M 5 advance The distance between these two sentences is equal to the distance between the sequences M 1 M 2 M 3 M 4 and M 1 M2M 3 M 5 , given the unit distance matrix D (Mj, M j ) calculated previously .
• the implementation of the invention is possible on a commercial computer, of any type provided with conventional interfaces for input and restitution of data (keyboard, mouse, screen, printer). Integration with a voice recognition system is possible on a common configuration.
• the computer system also has a microphone, speakers, a specialized signal processing card and specialized voice recognition software.

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• General Health & Medical Sciences (AREA)
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• General Engineering & Computer Science (AREA)
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