Translation correction of English phrases based on optimized GLR algorithm

2.1 Construction of phrase corpus

Table 1 describes the phrase information of the algorithm. The size of corpus phrases is about 580,000 English and Chinese words, which can roughly form 20,000 sentences and 10,000 sentences, while standard corpus sentences can only form 10,000 sentences. As shown in Table 1, the phrase corpus considers the scope and system of the corpus and can represent various communications in daily communication, business, technology, and machinery [26]. On the other hand, the corpus semantic method uses data, hierarchical structure, and processing methods to define sentences in text form, knows the various parts of short sentences to complete the sequence of sentences, and uses independent human–computer interaction to copy and calibrate English–Chinese translation sentences. The example illustrates the specific application of the oracle library.

Original sentence: I know you are a machine factory of physics facilities.

Part-of-speech tag: I/Verb know/INC you/PRP are/NNSa/V BP machine/VBP factory/NNS of/INP physics/JJ facilities/NNS./

2.2 Part-of-speech recognition of phrase corpus

Part-of-speech recognition is an important processing step of the automatic phrase analysis algorithm for English–Chinese machine translation, which is suitable for large-scale sentences, sentence, and grammatical ambiguities. According to the meaning of the phrase, divide the sentence into several words, arrange the words to form the relationship between the sentence and the person, and write independence in the phrase after acknowledging part of the sentence and forming a syntactic tree, which reduces English–Chinese machine translation. This is very useful for improving the efficiency of phrase processing. GLR is the basic algorithm for recognizing speech parts. It is a method of analyzing the probability of a phrase. The number of recognition results given by the standard GLR algorithm is uncertain, and there may be overlapping data points in different recognition results and the recognition accuracy is usually low. In the designed automatic sentence analysis algorithm for English–Chinese machine translation, the standard GLR algorithm is improved and a phrase structure centered on the phrase center is established to improve the accuracy of recognition.

2.3 Automatic phrase recognition algorithm correction process for English–Chinese machine translation

In the previous automatic algorithm of English machine translation proverbs, the discourse recognition result of the phrase was the final result, but the discourse recognition did not improve the organizational ambiguity between English and Chinese. It is necessary to correct the result of acknowledging the words. In the GLR algorithm, a linear analysis table is used to identify sentence functions. In addition, linear table analysis has another function, namely syntactic recognition, which uses progress, reduction, acceptance, termination, and errors as evidence to analyze part of the speech analysis. Figure 1 is a flowchart of automatic phrase correction algorithm correction for English–Chinese machine translation.

Figure 1

Algorithm correction flowchart.

It can be seen from Figure 1 that reduction is very similar to progress actions. Reduction refers to the reshaping of grammatical recognition constraints, which means that the previous constraints are invalid or there are errors in the cycle. Progress means that there is no error in the ambiguity structure in this syntactic recognition. The part of the speech analysis result of the sentence in front of the cover is correct, and then the receiving cursor is called and sent out for use. Normally, the approval cursor and indicator will appear at the same time. If the two are only one of the algorithm streams, it indicates a loop error or an error in the algorithm configuration. It is appropriate to review the analysis of the linear table to recover the acceptable part of the speech analysis result. Before replacing the converter, check the type of cursor. If the search fails, enter and complete the cursor. The end point was created at the preparation site, and the structure of the site may be ambiguous. After displaying the endpoints, the algorithm will build a sentence tree, mark the symbol table, analyze whether the central symbol of the prepared point exists, and place it on the correct sentence type. If it does not exist or the position is incorrect, the algorithm immediately calls the wrong cursor to correct it. In the entire algorithm correction process, there are multiple sentence analysis output ports, and a receiving cursor can only send one recognition result at a time. When multiple confirmation results need to be sent at the same time (because the two phrases in the sentence are continuous), please write multiple confirmation results into the same node of the phrase tree, and the recipient cursor will automatically use them as the confirmation result.

3.1 The structure of the analyzer

The structure of the entire basic equipment, it contains two diagnostic tables, one of which contains many export products. One is the Tab1eA diagnostic table, which is closely related to noun phrases that can identify and publish noun phrases* NP, adjective phrases* AjP, and adverbs* AvP: the other is Tab1eB speech analysis table, which can recognize and send unlimited sentences** Tobe; currently, Word segmentation/type sentences *Ving and proverb sentences *Ven and *Be, and *PPY} are necessary for in-depth grammatical analysis. The reason for this division is to fully consider the amount of connection between different sentences and reduce the complexity of the analyzer. Since the analysis method structure is used in the two analysis tables, each part of the voice sequence number is no longer consistent with the sequence number of the sequence status symbol in each analysis table, so it is necessary to classify the flight symbols into system status symbols and connections table. The system cover refers to the input symbols of the analyzer, and the diagnosis table cover refers to the error diagnosis table. We have introduced character mapping function for each diagnosis table to realize the conversion from system station to diagnosis table station. For the diagnostic table, if a system interrupt is invalid in the diagnostic table, the symbol mapping function will map it to the diagnostic table of the workstation (Figure 2).

Figure 2

Shallow syntactic analyzer structure.

3.2 Analysis algorithm based on GLR

Each diagnostic table of the analyzer and the diagnostic process of each diagnostic tablet are similar to the GLR algorithm, but with the following differences:

1. There are five functions in the analysis table, including the meanings “1v in,” “decrease,” and “keep unchanged.” There are two types of errors: “dismissal” and “resolve errors.” For the service error of the final investigation diagnosis table, “common error” refers to the service error of the stop diagnosis table. The “Finish” function does not regard the analysis as a failure, but keeps the current state unchanged, and returns the current winding system of the system to the suspension diagnosis table, and then continues the analysis; the “error” in the diagnosis table is considered a diagnosis failure. And restore the diagnosis cursor to the diagnosis position, and then end the analysis of the diagnosis table.

2. When trying to use rules for reduction, please check the restrictions of the rules; if the conditional Boolean activity value is true, then perform the reduction, otherwise perform the termination operation;

3. The analysis table is mostly exported, that is, there are many countries at the end of the analysis table.

Perform the “receive” function, each “receive” corresponds to a known phrase on the top of the symbol board. In the analysis, symbol keys and status keys are used, and they both perform push and pop operations at the same time. The token key holds a tree representing the sibling children of the token. Figure 3 shows the analysis algorithm based on GLR. The analysis steps are described as follows:

Figure 3

Syntactic analysis algorithm based on GLR.

Initialization. Push status 0 into the stack, analyze the status of the system station indicated by the cursor, and clear the termination flag.

1. Modification of the closing device. If the termination flag is not set, use the mapping function icon to map the current system cover to the diagnostic table or cover. If the termination flag is set, please directly map the current termination of the system on the diagnosis table.

Check the ACTION table and process each input function separately.

Input: Put the current state and current symbology (terminal and nonterminal) into the stack, and lower the diagnostic cursor.

Skip Code of Conduct: Call the precondition function to check the precondition. If the conditions are met, click the sub-node from the icon key to form the syntactic structure of the current icon tree. The grammatical structure is not closed and push the icon key; click the middle position state, check the GOTO table, and push the new state into the state pile. At the same time, point the middle word to the corresponding middle word and create a translation of the current irrelevant symbols according to the translation method. If the conditions are not met, the termination flag is set.

Termination: Set the termination flag.

Receive: Pop up the syntax of the tree at the top of the icon key, and then return.

Error: Restore the initial state and return, the instructions go to step 2.

During the execution of the plot, we checked the limitations of the syntactic rules. Limitations mainly examine the four attributes of the word itself, the subparts of the language, and its lexical and semantic technology. After further generalization, the author believes that it is possible to gradually study methods of expanding discourse labels before analysis. For example, based on semantic and morphological characteristics, these nouns can be expanded into time nouns n time, address nouns n-tle>, noun possessive epos), and common nouns to bypass nouns. For example, the phrase “to be corrected}, the clock started to work well.” The phonetic sequence after the meaning of the voice part is artificial/aux/verb} EN}/comma/art/noun/verb} ED}/to/verb/adv/pmark,” as shown in Figure 3. Therefore, after a part of the case is expanded, part of the case will become tracking/being/ven/comma/art/noun/ved./verb/adv/pmark” as shown in Figure 3. The actions and their GOTO tables are shown in Tables 2 and 3. For each rule in the analysis table, an underscore is used to indicate the middle word of a word that does not end in the left part of the rule, and some rules do not exist because these irrelevant symbols are neither recognizable sentences, nor will they become The middle word of the known sentence:

Figure 4

The symbolic mapping from the system terminator to the analysis table terminator.

Part-of-speech sequence after part-of-speech tagging:

1. The word order after the part of speech is expanded, so the middle word of the term is meaningless. In addition, the meaning of each rule is not enumerated one by one. Now take the various rules of the Tab1eA analysis table as an example. The third rule * NP -> /noun/number translation method can be described as “/number/noun,” produced by this rule. The translation of SNP is num across nouns). *NP -> /pron/The independent translation method is wool apron. It should be noted that the mapping shown in Figure 4 does not involve mapping. If the termination flag is set, each system shutter will be mapped to the termination analysis table.

4.1 Evaluation method

The experiment organized an evaluation team to evaluate the performance of the English subtitle automatic sentence analysis algorithm designed in this article, including the accuracy of word analysis, recognition speed, and update ability. The evaluation team includes three English–Chinese machine translators, two English–Chinese translators, and two ratings. The three English–Chinese machine translations have the same specifications and are equipped with algorithms, statistical algorithms, and weaker algorithms after initialization. The evaluation method adopts closed evaluation and development evaluation. Closed assessment refers to the automatic recognition of phrases in specific English–Chinese translation sentences; the English–Chinese translation sentences in the development assessment are randomly selected from the Internet. After using three automatic sentence analysis algorithms to identify and communicate the translation results, two English–Chinese translators communicated to translate. The scorer compares machine translation and manual translation and scores these three algorithms according to the scoring rules. The scoring rules are as follows:

1. The accuracy of the recognition algorithm, recognition speed, and update capability are, respectively, 90, 5, and 5% of the total level.

2. The certification accuracy scoring rules (not based on input errors) are as follows:

   1. 100 points: The meaning of the translation structure is very accurate, and the grammatical structure does not need to be changed;

   2. 80 points: The overall explanation is clear; there are minor grammatical errors; if necessary, some simple modifications should be made;

   3. 60 points: The overall explanation is clear; there are many grammatical errors that must be changed, otherwise the meaning is not clear;

   4. 40 points: Part of the explanation is clear, there is no obvious error in the expression, and the overall explanation is not continuous;

   5. 20 points: All and part of the explanation is very confusing, and the sentence explanation is obviously wrong;

   6. 0 points: The whole explanation and part of the explanation are very confusing and difficult to understand.

3. The recognition rules of recognition speed and update ability use weighted average method, that is, the total recognition time and total update time of the algorithm are multiplied by the weight, and then the sum is divided by the number of phrase analysis.

4.2 Evaluation results

The experiment performed phrase analysis on 60 sentences in closed assessment and development assessment. The evaluation results of the three algorithms are shown in Tables 2 and 4. The algorithm in this article has the highest score in the result evaluation, with an average of 92.3 points, while the lowest is the statistical algorithm with 75.1 points. The evaluation result of the dynamic memory algorithm is 91.2 points, which is not much different from the algorithm level in this article, but the update capability of the dynamic memory algorithm is seriously insufficient. From a long-term perspective, the algorithm in this article is more practical.