Method for automatically translating text by AI (Artificial Intelligence)

Abstract

The invention discloses a method for automatically translating a text through an AI. The method for automatically translating the text through the AI comprises the following steps that (1) source Chinese sentences are input; (2) preprocessing the vocabularies; (3) phrases and grammar are analyzed; (4) judging polysemy words; (5) translating the prepositions; (6) judging and processing the ambiguity; (7) performing vocabulary translation; (8) reordering is carried out; and (9) outputting a target statement, in the step (1), inputting a source Chinese sentence needing to be translated into an AI translation system, and constructing the AI translation system by adopting a deep learning technology and a neural network model, so that language data can be automatically translated, the function of instant translation and instant use is realized, and the translation efficiency is improved. Further, the voice can be understood in a deeper level, so that the translation is more accurate and smoother, and the translation can more accurately fit the expression habit especially for complex contexts and slang habit usage.

Description

Technical Field

[0001] The present application relates to the field of AI translation technology, and in particular to a method for automatic text translation by AI. Background Art

[0002] "Automatic translation" refers to the process of automatically converting text in one language into another language using computer technology and natural language processing technology. This technology is usually called machine translation (MT). The automatic translation system can analyze the grammar and vocabulary of the source language and try to generate text with the same or similar meaning in the target language.

[0003] The existing patent document "CN105242932B A method for automatically translating software developed based on DELPHI tool" discloses an automatic translation method. The automatic translation method in the above technical solution can automatically translate languages, but when applied to complex contexts, slang and other languages ​​for translation, it is difficult to fully fit the expression habits;

[0004] That is, the existing technology has the following technical problems: ordinary translation methods are difficult to fully fit the expression habits when translating complex contexts, slang and other languages. Therefore, a method of AI automatic text translation is proposed to address the above problems. Summary of the invention

[0005] In this embodiment, an AI automatic text translation method is provided to solve the problem that ordinary translation methods in the prior art are difficult to fully adapt to expression habits when translating languages ​​such as complex contexts and slang.

[0006] According to one aspect of the present application, a method for automatic text translation by AI is provided, and the method for automatic text translation by AI comprises the following steps:

[0007] (1) Source language sentence input;

[0008] (2) Preprocessing the vocabulary;

[0009] (3) Analyze phrases and grammar;

[0010] (4) Judge polysemous words;

[0011] (5) Translate prepositions;

[0012] (6) Judge and deal with ambiguity;

[0013] (7) Perform vocabulary translation;

[0014] (8) re-arrange;

[0015] (9) Target sentence output.

[0016] Furthermore, in step (1), the source language sentence to be translated is input into an AI translation system, and the AI ​​translation system is constructed using deep learning technology and a neural network model.

[0017] Furthermore, in the step (2), the vocabulary is preprocessed, and the preprocessing includes the following steps:

[0018] a. High-frequency words and unit words screening: Use high-frequency word dictionaries and unit word dictionaries to quickly identify and mark common words in sentences;

[0019] b. Alphabetical sorting and homograph processing: sort the words alphabetically for easy retrieval; identify and process homographs to ensure accurate reversal;

[0020] c. Compound noun recognition: Analyze vocabulary relevance and context to identify compound nouns.

[0021] Furthermore, in step (3), a phrase grammatical structure analysis is performed, and the analysis includes the following steps:

[0022] a. Phrase recognition: using the phrase rule library to mark the short sentence structure in the sentence;

[0023] b. Grammatical relationship analysis, analyzing the basic grammatical structure in the sentence, the basic grammatical structure includes: subject, predicate, object, attributive, adverbial and complement;

[0024] c. Parallel structure processing: identifying and processing parallel elements in sentences;

[0025] d. Identify the subject and predicate in the sentence;

[0026] e. Prepositional structure analysis: identify and parse the prepositional structure in the sentence.

[0027] Furthermore, in the step (4), the polysemous words are judged by using a context rule dictionary and combining the context to judge the specific meaning of the polysemous words and perform translation.

[0028] Furthermore, in step (5), preposition translation and grammar adjustment are performed, and the prepositions given in the sentence are translated according to the grammatical rules of the target language, and the grammatical structure is adjusted to adapt to the target language.

[0029] Furthermore, in step (6), ambiguity judgment and elimination are performed by analyzing the sentence to determine whether ambiguity exists, and the ambiguity is eliminated by adding annotations, adjusting word order, and selecting other words.

[0030] Furthermore, in step (7), vocabulary translation is performed. During translation, the entire dictionary resources are used to accurately translate each word in the sentence, and verification is performed to ensure accuracy. A deep learning model is used to perform a deeper semantic understanding and generation of the sentence, thereby improving the accuracy and fluency of the translation.

[0031] Furthermore, in step (8), word order adjustment and sentence construction are performed, and the translated words are rearranged and combined according to the grammatical rules and expression habits of the target language to construct grammatically consistent and fluent target language sentences.

[0032] Furthermore, in step (9), the translated target sentence is output, and a final check and correction is performed, and the user's language preference and professional background data are analyzed to perform professional terminology translation and cultural adaptation adjustment.

[0033] Through the above technical solutions of the present application, the present application can automatically translate language data, realize the function of instant translation and instant use, and further can have a deeper understanding of the voice, thereby making the translation more accurate and fluent, especially for complex contexts and slang idioms, which can make the translation more accurate and in line with the expression habits. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 The figure is a schematic diagram of the overall process of an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0039] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0040] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "socketed" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection, or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0041] See also Figure 1 As shown, the method of AI automatic text translation includes the following steps:

[0042] (1) Source language sentence input;

[0043] (2) Preprocessing the vocabulary;

[0044] (3) Analyze phrases and grammar;

[0045] (4) Judge polysemous words;

[0046] (5) Translate prepositions;

[0047] (6) Judge and deal with ambiguity;

[0048] (7) Perform vocabulary translation;

[0049] (8) re-arrange;

[0050] (9) Target sentence output.

[0051] In the step (1), the source sentence to be translated is input into an AI translation system, and the AI ​​translation system is constructed using deep learning technology and a neural network model;

[0052] In the step (2), the vocabulary is preprocessed, and the preprocessing includes the following steps:

[0053] a. High-frequency words and unit words screening: Use high-frequency word dictionaries and unit word dictionaries to quickly identify and mark common words in sentences;

[0054] b. Alphabetical sorting and homograph processing: sort the words alphabetically for easy retrieval; identify and process homographs to ensure accurate reversal;

[0055] c. Compound noun recognition: Analyze vocabulary relevance and context to identify compound nouns.

[0056] In the step (3), a phrase grammatical structure analysis is performed, and the analysis includes the following steps:

[0057] a. Phrase recognition: using the phrase rule library to mark the short sentence structure in the sentence;

[0058] b. Grammatical relationship analysis, analyzing the basic grammatical structure in the sentence, the basic grammatical structure includes: subject, predicate, object, attributive, adverbial and complement;

[0059] c. Parallel structure processing: identifying and processing parallel elements in sentences;

[0060] d. Identify the subject and predicate in the sentence;

[0061] e. Prepositional structure analysis, to identify and parse the prepositional structure in the sentence;

[0062] In the step (4), the polysemous words are judged by using a context rule dictionary and combining the context to judge the specific meaning of the polysemous words and perform translation;

[0063] In the step (5), preposition translation and grammar adjustment are performed, and the prepositions given in the sentence are translated according to the grammatical rules of the target language, and the grammatical structure is adjusted to adapt to the target language;

[0064] In step (6), ambiguity judgment and elimination are performed, the sentence is analyzed to determine whether there is ambiguity, and the ambiguity is eliminated by adding annotations, adjusting word order, and selecting other words;

[0065] In the step (7), vocabulary translation is performed. During the translation, the entire dictionary resources are used to accurately translate each word in the sentence, and verification is performed to ensure accuracy. The deep learning model is used to perform a deeper semantic understanding and generation of the sentence, thereby improving the accuracy and fluency of the translation;

[0066] In the step (8), the word order is adjusted and the sentence is constructed. According to the grammatical rules and expression habits of the target language, the translated words are reordered and reassembled to form a grammatically and fluent target language sentence.

[0067] In the step (9), the translated target sentence is output, and a final check and correction is performed, and the user's language preference and professional background data are analyzed to perform professional terminology translation and cultural adaptation adjustment;

[0068] This application can automatically translate language data, realize the function of instant translation and instant use, and further can have a deeper understanding of speech, thereby making the translation more accurate and fluent, especially for complex contexts and slang idioms, which can make the translation more accurate and in line with the expression habits.

[0069] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. The method of AI automatic translation of text is characterized by: The method of automatically translating text by AI comprises the following steps: (1) Source language sentence input; (2) Preprocessing the vocabulary; (3) Analyze phrases and grammar; (4) Judge polysemous words; (5) Translate prepositions; (6) Judge and deal with ambiguity; (7) Perform vocabulary translation; (8) re-arrange; (9) Target sentence output.

2. The method for automatic text translation using AI according to claim 1, characterized in that: In the step (1), the source sentence to be translated is input into an AI translation system, and the AI ​​translation system is constructed using deep learning technology and a neural network model.

3. The method for automatic text translation using AI according to claim 1, characterized in that: In the step (2), the vocabulary is preprocessed, and the preprocessing includes the following steps: a. High-frequency words and unit words screening: Use high-frequency word dictionaries and unit word dictionaries to quickly identify and mark common words in sentences; b. Alphabetical sorting and homograph processing: sort the words alphabetically for easy retrieval; identify and process homographs to ensure accurate reversal; c. Compound noun recognition: Analyze vocabulary relevance and context to identify compound nouns.

4. The method for automatic text translation using AI according to claim 1, characterized in that: In the step (3), a phrase grammatical structure analysis is performed, and the analysis includes the following steps: a. Phrase recognition: using the phrase rule library to mark the short sentence structure in the sentence; b. Grammatical relationship analysis, analyzing the basic grammatical structure in the sentence, the basic grammatical structure includes: subject, predicate, object, attributive, adverbial and complement; c. Parallel structure processing: identifying and processing parallel elements in sentences; d. Identify the subject and predicate in the sentence; e. Prepositional structure analysis: identify and parse the prepositional structure in the sentence.

5. The method of AI automatic text translation according to claim 1, characterized in that: In the step (4), the polysemous words are judged by using a context rule dictionary and combining the context to judge the specific meaning of the polysemous words and perform translation.

6. The method of AI automatic text translation according to claim 1, characterized in that: In the step (5), preposition translation and grammar adjustment are performed. According to the grammatical rules of the target language, the prepositions given in the sentence are translated, and the grammatical structure is adjusted to adapt to the target language.

7. The method of AI automatic text translation according to claim 1, characterized in that: In step (6), ambiguity judgment and elimination are performed by analyzing the sentence to determine whether there is ambiguity, and the ambiguity is eliminated by adding annotations, adjusting word order, and selecting other words.

8. The method of AI automatic text translation according to claim 1, characterized in that: In the step (7), vocabulary translation is performed. During the translation, the entire dictionary resources are used to accurately translate each word in the sentence, and verification is performed to ensure accuracy. The deep learning model is used to perform a deeper semantic understanding and generation of the sentence, thereby improving the accuracy and fluency of the translation.

9. The method for automatic text translation using AI according to claim 1, characterized in that: In the step (8), word order adjustment and sentence construction are performed, and the translated words are reordered and reassembled according to the grammatical rules and expression habits of the target language to form grammatically consistent and fluent target language sentences.

10. The method of AI automatic text translation according to claim 1, characterized in that: In the step (9), the translated target sentence is output, and a final check and correction is performed, and the user's language preference and professional background data are analyzed to perform professional terminology translation and cultural adaptation adjustment.

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