阅读说明：本技术 一种区分文本信息的方法 (Method for distinguishing text information ) 是由 周继敏 于 2019-10-14 设计创作，主要内容包括：本发明公开了一种区分文本信息的方法,所述方法包括：S1：接收所述文本信息。S2：识别所述文本信息为所述书面文本相关信息或所述口语文本相关信息,并将所述书面文本相关信息发送到与其对应的文本模型或者将所述口语文本相关信息发送到与其对应的语言模型。S3：判断所述S2中的所述文本模型、所述语言模型的识别结果,根据所述识别结果判断是否进行再次识别。达到了识别口头(言语)和书面(文字)的细微差别,正确且完整识别口头(言语)和书面(文字),使企业能够更准确地理解和回复这两种通信形式,真实还原面对面的有效沟通的技术效果。(The invention discloses a method for distinguishing text information, which comprises the following steps: s1: and receiving the text information. S2: and identifying the text information as the relevant written text information or the relevant spoken text information, and sending the relevant written text information to a text model corresponding to the relevant written text information or sending the relevant spoken text information to a language model corresponding to the relevant spoken text information. S3: and judging the recognition results of the text model and the language model in the step S2, and judging whether to perform re-recognition according to the recognition results. The method achieves the effects of identifying the nuance of the oral (speech) and written (characters), correctly and completely identifying the oral (speech) and written (characters), so that enterprises can more accurately understand and reply the two communication forms, and the technical effect of effective communication in face-to-face is really restored.)

1. A method of distinguishing textual information, the method comprising:

s1: receiving text information;

s2: identifying the text information as written text related information or spoken text related information, and sending the written text related information to a text model corresponding to the written text related information or sending the spoken text related information to a language model corresponding to the spoken text related information;

s3: and judging the recognition results of the text model and the language model in the step S2, and judging whether to perform re-recognition according to the recognition results.

2. The method according to claim 1, wherein said identifying said text information as written text related information or spoken text related information in said S2 comprises: recognizing the text message by using an external engine, and determining whether the text message received in S1 is a written text message or a spoken text message.

3. The method according to claim 1, wherein the determining the recognition result of the text model and the language model in the S2 in the S3 comprises: and judging whether the text information result identified by the text model is correct or not and judging whether the text information result identified by the language model is correct or not.

4. The method according to claim 3, wherein the determining whether to perform re-recognition according to the recognition result in S3 includes: if the text information recognized by the text model is incorrect, recognizing the text information by using a language model; and if the text information recognized by the language model is incorrect in result, recognizing the text information by using the text model.

5. The method of claim 1, further comprising:

s4: and updating parameters related to the text model and the language model according to the recognition results of the text model and the language model.

Technical Field

The invention belongs to the technical field of artificial intelligent natural language processing, and particularly relates to a method for distinguishing text information.

Background

Oral (speech) and written (text) are the two most important means of communication, especially in a commercial setting. The traditional natural language processing method is a simple extraction and analysis of a given text, regardless of the source of the text. However, there are many subtle differences in determining the meaning of each communication modality. For example, people often use filler words in verbal communication or make some sound ("e", "hmm", "en", clear throat, etc.) that never gets written. There are also some different meanings associated with sound that are never written down ("yes" meaning consent, "nozzle" meaning less consent). Also, there are many symbols (punctuation, symbolic symbols, emoticons, etc.) that often appear in written text, which are never spoken but may be important to understand meaning. People tend to respond verbally with more complete sentences, while with shorter phrases, how to recognize nuances of oral (speech) and written (text), correctly and completely recognize oral (speech) and written (text), truly reverting to effective face-to-face communication.

Disclosure of Invention

Aiming at the defects in the prior art, the embodiment of the invention provides a method for distinguishing written texts from spoken texts, so that the nuances of oral (speech) and written (text) are recognized, the oral (speech) and written (text) are recognized correctly and completely, enterprises can understand and reply the two communication forms more accurately, and the technical effect of effective face-to-face communication is truly restored.

In view of the above technical problems, an embodiment of the present invention provides a method for distinguishing text information, where the method includes:

s1: and receiving the text information.

S2: and identifying the text information as the relevant written text information or the relevant spoken text information, and sending the relevant written text information to a text model corresponding to the relevant written text information or sending the relevant spoken text information to a language model corresponding to the relevant spoken text information.

S3: and judging the recognition results of the text model and the language model in the step S2, and judging whether to perform re-recognition according to the recognition results.

According to an embodiment of the present invention, the identifying the text information as the written text related information or the spoken text related information in S2 includes: recognizing the text information using an external engine, and determining whether the text information received in S1 is the written text information or the spoken text information.

According to an embodiment of the present invention, the determining the recognition result of the text model and the language model in S2 in S3 includes: and judging whether the text information result identified by the text model is correct or not and judging whether the text information result identified by the language model is correct or not.

According to an embodiment of the present invention, the determining whether to perform re-recognition according to the recognition result in S3 includes: if the text information recognized by the text model is incorrect in result, recognizing the text information by using a language model; and if the text information recognized by the language model is incorrect in result, recognizing the text information by using the text model.

According to one embodiment of the present invention, S4: and updating parameters related to the text model and the language model according to the recognition results of the text model and the language model.

The invention achieves the technical effects that: the present invention is a method of classifying text as originating from speech or words and analyzing it using a separate model. Using a large number of written text and speech samples, we trained two independent models using machine learning and Python text classification. To accomplish this, speech conversion to text uses speech-to-text techniques. In application, the input phrases are received by an external engine located outside the training model, which can recognize the input source and assign it to the appropriate model for NLP analysis. However, this task is not a constant one, it is only a priority; the analysis from the assigned model turns out to be inaccurate and the input will automatically be transferred to another model for analysis. As with machine learning systems, each input further improves the accuracy of the model. The machine can determine the meaning of the input phrase and use it for artificial intelligence applications. The method aims at the nuances to train, so that enterprises can more accurately understand and reply the two communication forms, the communication invalidation caused by inaccurate machine translation is avoided, and meanwhile, the cooperative effectiveness in the working process can be greatly improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

FIG. 1 is a flow chart of a method of an embodiment of the present invention;

FIG. 2 is a flow diagram of yet another method of an embodiment of the present invention;

fig. 3 is a flow chart of yet another method of an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without any creative effort belong to the protection scope of the embodiments of the present invention.

The embodiment of the invention provides a method for distinguishing written texts from spoken texts, which achieves the purposes of identifying nuances of oral words (speeches) and written texts, correctly and completely identifying the oral words (speeches) and the written texts, so that enterprises can more accurately understand and reply the two communication forms, and the technical effect of effective face-to-face communication is really restored.

An embodiment of the present invention provides a method for distinguishing text information, as shown in fig. 1, the method includes:

s1: and receiving the text information.

S2: and identifying the text information as the relevant written text information or the relevant spoken text information, and sending the relevant written text information to a text model corresponding to the relevant written text information or sending the relevant spoken text information to a language model corresponding to the relevant spoken text information.

S3: and judging the recognition results of the text model and the language model in the step S2, and judging whether to perform re-recognition according to the recognition results.

According to an embodiment of the present invention, the identifying the text information as the written text related information or the spoken text related information in S2 includes: recognizing the text information using an external engine, and determining whether the text information received in S1 is the written text information or the spoken text information.

According to an embodiment of the present invention, the determining the recognition result of the text model and the language model in S2 in S3 includes: and judging whether the text information result identified by the text model is correct or not and judging whether the text information result identified by the language model is correct or not.

According to an embodiment of the present invention, the determining whether to perform re-recognition according to the recognition result in S3 includes: if the text information recognized by the text model is incorrect in result, recognizing the text information by using a language model; and if the text information recognized by the language model is incorrect in result, recognizing the text information by using the text model.

According to an embodiment of the invention, as shown in fig. 2, the method further comprises: s4: and updating parameters related to the text model and the language model according to the recognition results of the text model and the language model.

As shown in fig. 3, a flowchart of another method disclosed in the embodiment of the present invention includes:

1.0: a given phrase (speech or written text) is entered.

2.0: the engine determines the source of the input phrase (speech or written text) and assigns it to the appropriate model.

3.0: the model analyzes the text according to machine learning training to determine the meaning of the text.

4.0: if the analysis is inaccurate, another model will be used to analyze the input.

5.0: each new sample was used to refine the model.

The invention achieves the technical effects that: the present invention is a method of classifying text as originating from speech or words and analyzing it using a separate model. Using a large number of written text and speech samples, we trained two independent models using machine learning and Python text classification. To accomplish this, speech conversion to text uses speech-to-text techniques. In application, the input phrases are received by an external engine located outside the training model, which can recognize the input source and assign it to the appropriate model for NLP analysis. However, this task is not a constant one, it is only a priority; the analysis from the assigned model turns out to be inaccurate and the input will automatically be transferred to another model for analysis. As with machine learning systems, each input further improves the accuracy of the model. The machine can determine the meaning of the input phrase and use it for artificial intelligence applications. The method aims at the nuances to train, so that enterprises can more accurately understand and reply the two communication forms, the communication invalidation caused by inaccurate machine translation is avoided, and meanwhile, the cooperative effectiveness in the working process can be greatly improved.

Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

The above-described embodiments of the electronic device and the like are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may also be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the embodiments of the present invention, and are not limited thereto; although embodiments of the present invention have been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the respective technical solutions of the embodiments of the present invention.