Intelligent college English auxiliary teaching method and system based on artificial intelligence

Abstract

The invention discloses a college English intelligent auxiliary teaching method and system based on artificial intelligence, and the method comprises the steps: extracting text information in a college English textbook, and setting an element information extraction function which is used for extracting the element information of each element in the text information; setting a relation extraction function for extracting a relation between each element of the text information and other elements; setting an importance scoring function for extracting an importance score of each element in the text information; setting a context function for extracting the context of the context information in the text information; and according to the element information of each element, the relationship between each element and other elements, the importance score of each element and the context of the context information, generating a knowledge graph of the college English textbook, and carrying out teaching according to the knowledge graph of the college English textbook.

Description

Technical Field

[0001] The present invention belongs to the technical field of knowledge graphs, and more specifically, relates to an intelligent assisted teaching method and system for college English based on artificial intelligence. Background Art

[0002] At present, college English teaching is carried out by teachers making courseware or PPTs by themselves. First, teachers need to fully understand the college English textbooks and the key points in the textbooks in order to make relevant courseware or PPTs. This leads to low teaching efficiency and the teaching quality cannot be guaranteed.

[0003] Therefore, there is an urgent need for a technical solution that can improve the efficiency and quality of English teaching. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes an intelligent assisted teaching method for college English based on artificial intelligence, including:

[0005] Extract the text information in the college English textbooks, and set an element information extraction function for extracting the element information of each element in the text information;

[0006] Set a relationship extraction function for extracting the relationship between each element in the text information and other elements;

[0007] Set an importance scoring function for extracting the importance score of each element in the text information;

[0008] Set a context function for extracting the context of the context information in the text information;

[0009] Generate a knowledge graph of the college English textbooks according to the element information of each element, the relationship between each element and other elements, the importance score of each element, and the context of the context information, and conduct teaching according to the knowledge graph of the college English textbooks.

[0010] Further, generating the knowledge graph of the college English textbooks includes:

[0011]

[0012] Among them, For the finally generated knowledge graph, n is the number of elements, Ω(E i ) is the element information extraction function of the i-th element E i in the text information, and the elements include: words, phrases, sentences, and / or paragraphs, Ω(R i ) is the relationship extraction function of the relationship R i between the i-th element and other elements in the text information, Ω(Pi ) is the importance score $P$ of the $i$-th element in the text information i of the importance scoring function, $\Omega(S$ t ) is the context information $S$ in the text information t of the context function.

[0013] Furthermore, the element information extraction function $\Omega(E$ i ) of the $i$-th element $E$ in the text information includes: i ) includes:

[0014]

[0015] where $\alpha$ 1 is the first adjustment factor of the element information extraction function, $Dist(E$ i , $S$ t ) is the distance between the $i$-th element $E$ i and the context information $S$ in the text information t , $\beta$ 1 is the second adjustment factor of the element information extraction function, $\gamma$ 1 is the third adjustment factor of the element information extraction function, $\gamma$ 2 is the fourth adjustment factor of the element information extraction function, $\delta$ 1 is the fifth adjustment factor of the element information extraction function, $A$ t is the structural information of the text information.

[0016] Furthermore, the relationship extraction function $\Omega(R$ i ) of the relationship $R$ between the $i$-th element and other elements in the text information includes: i ) includes:

[0017]

[0018] where $\alpha$ 2 is the first adjustment factor of the relationship extraction function, $Sim(E$ i , $E$ j ) is the semantic similarity between the $i$-th element $E$ i and the $j$-th element $E$ in the text information j , $\beta$ 2 is the second adjustment factor of the relationship extraction function, $\delta$ 2 is the third adjustment factor of the relationship extraction function, $\kappa$ 1 is the fourth adjustment factor of the relationship extraction function, $Sentiment(E$ i , $E$ j ) is the sentiment between the $i$-th element $E$ i and the $j$-th element $E$ in the text information jThe sentiment analysis metric is used to capture the i-th element E in the text information i and the j-th element E in the text information j The sentiment tendency between them

[0019] Furthermore, the importance score P of the i-th element in the text information i The importance score function Ω(P i ) includes

[0020]

[0021] Among them, α 3 Is the first adjustment factor of the importance score function, β 3 Is the second adjustment factor of the importance score function, Frequency(E i ) is the frequency of the i-th element E in the text information i Appearing in the text information, γ 2 Is the third adjustment factor of the importance score function, κ 2 Is the fourth adjustment factor of the importance score function, ValueScore(E i ) is the value score of the i-th element E in the text information i Used to score the value of the i-th element in the text information based on the structure and key chapters of the college English textbook

[0022] Furthermore, the context context function Ω(S t ) of the context information S in the text information includes t ) includes

[0023]

[0024] Among them, m is the number of paragraphs, λ 1 Is the first adjustment factor of the context context function, ContextSimilarity(S t , C i ) is the context similarity between the context information S in the text information t And the i-th paragraph C in the text information i , λ 2 Is the second adjustment factor of the context context function, δ 3 Is the third adjustment factor of the context context function, κ 3 Is the fourth adjustment factor of the context context function, TemporalEffect(C i ) is the temporal effect of the i-th paragraph C in the text information i At different reading stages

[0025] The present invention also provides an intelligent assisted teaching system for college English based on artificial intelligence, including:

[0026] An element information extraction module, which is used to extract the text information in the college English textbook, and set an element information extraction function to extract the element information of each element in the text information;

[0027] A relationship extraction module, which is used to set a relationship extraction function to extract the relationship between each element in the text information and other elements;

[0028] An importance score extraction module, which is used to set an importance score function to extract the importance score of each element in the text information;

[0029] A context extraction module, which is used to set a context function to extract the context of the context information in the text information;

[0030] A knowledge graph generation module, which is used to generate a knowledge graph of the college English textbook according to the element information of each element, the relationship between each element and other elements, the importance score of each element, and the context of the context information, and conduct teaching according to the knowledge graph of the college English textbook.

[0031] Furthermore, generating the knowledge graph of the college English textbook includes:

[0032]

[0033] Among them, is the finally generated knowledge graph, n is the number of elements, Ω(E i ) is the element information extraction function of the i-th element E i in the text information, and the elements include: words, phrases, sentences, and / or paragraphs, Ω(R i ) is the relationship extraction function of the relationship R i between the i-th element and other elements in the text information, Ω(P i ) is the importance score function of the importance score P i of the i-th element in the text information, and Ω(S t ) is the context function of the context information S t in the text information.

[0034] Furthermore, the element information extraction function Ω(E i ) of the i-th element E i in the text information includes:

[0035]

[0036] Among them, α 1is the first adjustment factor of the element information extraction function, Dist(E i , S t ) is the i-th element E in the text information i and context information S in text information t The distance, β 1 is the second adjustment factor of the element information extraction function, γ 1 is the third adjustment factor of the element information extraction function, γ 2 is the fourth adjustment factor of the element information extraction function, δ 1 is the fifth adjustment factor of the element information extraction function, A t It is the structural information of text information.

[0037] Furthermore, the relationship R between the i-th element and other elements in the text information i The relation extraction function Ω(R i )include:

[0038]

[0039] Among them, α 2 is the first adjustment factor of the relation extraction function, Sim(E i , E j ) is the i-th element E in the text information i and the jth element E in the text information j The semantic similarity of 2 is the second adjustment factor of the relation extraction function, δ 2 is the third adjustment factor of the relation extraction function, κ 1 is the fourth adjustment factor of the relation extraction function, Sentiment(E i , E j ) is the i-th element E in the text information i and the jth element E in the text information j The sentiment analysis metric is used to capture the i-th element E in the text information. i and the jth element E in the text information j The emotional inclination between them.

[0040] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0041] The present invention can provide teaching assistance to teachers by establishing a knowledge graph of college English textbooks, thereby improving teaching efficiency and teaching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0043] Figure 2 It is the system structure diagram of Embodiment 2 of the present invention. Detailed implementation manners

[0044] To better understand the above technical solutions, the following will describe the above technical solutions in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0045] The method provided by the present invention can be implemented in the following terminal environment. The terminal may include one or more of the following components: a processor, a storage medium, and a display screen. Among them, at least one instruction is stored in the storage medium, and the instruction is loaded and executed by the processor to implement the method described in the following embodiments.

[0046] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts within the entire terminal, and by running or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and by calling data stored in the storage medium, it executes various functions of the terminal and processes data.

[0047] The storage medium may include a random access memory (RAM), and may also include a read-only memory (ROM). The storage medium can be used to store instructions, programs, code, code sets, or instructions.

[0048] The display screen is used to display the user interfaces of various application programs.

[0049] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be elaborated here.

[0050] Embodiment 1

[0051] As Figure 1 shown, an intelligent assisted teaching method for college English based on artificial intelligence proposed in an embodiment of the present invention includes:

[0052] Step 101, extract the text information in the college English textbook, and set an element information extraction function for extracting the element information of each element in the text information;

[0053] Specifically, the element information extraction function Ω(E i of the i-th element E i ) in the text information includes:

[0054]

[0055] Among them, α 1 is the first adjustment factor of the element information extraction function, Dist(E i , S t ) is the distance between the i-th element E i and the context information S t in the text information, β 1 is the second adjustment factor of the element information extraction function, γ 1 is the third adjustment factor of the element information extraction function, γ 2 is the fourth adjustment factor of the element information extraction function, δ 1 is the fifth adjustment factor of the element information extraction function, and A t is the structural information of the text information.

[0056] Step 102: Set a relationship extraction function for extracting the relationship between each element of the text information and other elements;

[0057] Specifically, the relationship extraction function Ω(R i ) for the relationship R i between the i-th element and other elements in the text information includes:

[0058]

[0059] Among them, α 2 is the first adjustment factor of the relationship extraction function, Sim(E i , E j ) is the semantic similarity between the i-th element E i and the j-th element E j in the text information, β 2 is the second adjustment factor of the relationship extraction function, δ 2 is the third adjustment factor of the relationship extraction function, κ 1 is the fourth adjustment factor of the relationship extraction function, and Sentiment(E i , E j ) is the sentiment analysis metric between the i-th element E i and the j-th element E j in the text information, which is used to capture the sentiment tendency between the i-th element E i and the j-th element E j in the text information.

[0060] Preferably, the semantic similarity Sim(E i between the i-th element E jSentiment analysis metric of (E i , E j ), for example, using a known sentiment dictionary (such as SentiWordNet or VADER), estimate the sentiment score of the i-th element E i in the text information by querying the sentiment score of each word, and the sentiment analysis metric of the j-th element E j in the text information is Sentiment(E i , E j ).

[0061] Step 103, set an importance scoring function for extracting the importance score of each element in the text information;

[0062] Specifically, the importance scoring function Ω(P i ) of the importance score P i of the i-th element in the text information includes:

[0063]

[0064] Among them, α 3 is the first adjustment factor of the importance scoring function, β 3 is the second adjustment factor of the importance scoring function, Frequency(E i ) is the frequency of occurrence of the i-th element E i in the text information, γ 2 is the third adjustment factor of the importance scoring function, κ 2 is the fourth adjustment factor of the importance scoring function, and ValueScore(E i ) is the value score of the i-th element E i in the text information, which is used to score the value of the i-th element in the text information based on the structure and key chapters of a college English textbook.

[0065] Preferably, regarding the value score ValueScore(E i ) of the i-th element E i in the text information, if the i-th element E i belongs to a key chapter, the score is the highest. For example, if the i-th element E i belongs to a key chapter, the value score is 10 points. If the i-th element E i belongs to the chapter before or after the key chapter, the value score is 7 points, and in other cases, the value score is 5 points.

[0066] Step 104, set a context function for extracting the context of the context information in the text information;

[0067] Specifically, the context information S in the text information t The context function Ω(S t )include:

[0068]

[0069] Where m is the number of paragraphs, λ 1 is the first adjustment factor of the context function, ContextSimilarity(S t , C i ) is the context information S in the text information t and the i-th paragraph C in the text information i The context similarity of 2 is the second adjustment factor of the context function, δ 3 is the third adjustment factor of the context function, κ 3 is the fourth adjustment factor of the context function, TemporalEffect(C i ) is the i-th paragraph C in the text information i Timing effects at different stages of reading.

[0070] Preferably, the temporal learning model obtains the i-th paragraph C in the text information i Temporal Effect (C i ), specifically, the learning stage is usually an orderly process. As learning progresses, some information may become more important while other information may become less relevant. Therefore, the temporal effect is quantified by modeling the temporal evolution of the learning process.

[0071] For example, by defining a decay function that changes over time, assuming that t is the current learning stage or reading progress (for example, the number of days of learning, chapter progress, etc.), we can define a decay factor to represent the importance of information over time:

[0072] TemporalEffect(C i )=e -λ'·t ·Relevance(C i )

[0073] Among them, Relevance (C i ) is the i-th paragraph C in the text information i Raw importance at different reading stages, λ′ adjustment factor.

[0074] Step 105: Generate a knowledge graph of college English textbooks based on the element information of each element, the relationships between each element and other elements, the importance score of each element, and the context of the context information, and conduct teaching based on the knowledge graph of the college English textbooks.

[0075] Specifically, generating the knowledge graph of college English textbooks includes:

[0076]

[0077] Among them, is the finally generated knowledge graph, n is the number of elements, Ω(E i ) is the element information extraction function of the i-th element E i in the text information. The elements include: words, phrases, sentences, and / or paragraphs. Ω(R i ) is the relationship extraction function of the relationship R i between the i-th element and other elements in the text information. Ω(P i ) is the importance score function of the importance score P i of the i-th element in the text information. Ω(S t ) is the context function of the context information S t in the text information.

[0078] Example 2

[0079] As Figure 2 shown, an embodiment of the present invention also provides an intelligent auxiliary teaching system for college English based on artificial intelligence, including:

[0080] An element information extraction module, configured to extract text information from a college English textbook, and set an element information extraction function for extracting the element information of each element in the text information;

[0081] Specifically, the element information extraction function Ω(E i ) of the i-th element E i in the text information includes:

[0082]

[0083] Among them, α 1 is the first adjustment factor of the element information extraction function, Dist(E i , S t ) is the distance between the i-th element E i in the text information and the context information S t in the text information, β 1 is the second adjustment factor of the element information extraction function, γ 1 is the third adjustment factor of the element information extraction function, γ2 is the fourth adjustment factor for the element information extraction function, δ 1 is the fifth adjustment factor for the element information extraction function, A t is the structural information of the text information.

[0084] The extraction relationship module is used to set a relationship extraction function for extracting the relationship between each element of the text information and other elements;

[0085] Specifically, the relationship R between the i-th element and other elements in the text information i The relationship extraction function Ω(R i ) includes:

[0086]

[0087] Among them, α 2 is the first adjustment factor of the relationship extraction function, Sim(E i , E j ) is the semantic similarity between the i-th element E i and the j-th element E j in the text information, β 2 is the second adjustment factor of the relationship extraction function, δ 2 is the third adjustment factor of the relationship extraction function, κ 1 is the fourth adjustment factor of the relationship extraction function, Sentiment(E i , E j ) is the sentiment analysis metric between the i-th element E i and the j-th element E j in the text information, used to capture the sentiment tendency between the i-th element E i and the j-th element E j in the text information.

[0088] The extraction importance scoring module is used to set an importance scoring function for extracting the importance score of each element in the text information;

[0089] Specifically, the importance score P of the i-th element in the text information i The importance scoring function Ω(P i ) includes:

[0090]

[0091] Among them, α 3 is the first adjustment factor of the importance scoring function, β 3 is the second adjustment factor of the importance scoring function, Frequency(E i ) is the i-th element E in the text informationi The frequency of occurrence in the text information, γ 2 Is the third adjustment factor of the importance scoring function, κ 2 Is the fourth adjustment factor of the importance scoring function, ValueScore(E i ) is the value score of the i-th element E in the text information i Used to score the value of the i-th element in the text information based on the structure and key chapters of the college English textbook.

[0092] Extract the context module, used to set the context function, for extracting the context of the context information in the text information;

[0093] Specifically, the context information S in the text information t The context function Ω(S t ) includes:

[0094]

[0095] Among them, m is the number of paragraphs, λ 1 Is the first adjustment factor of the context function, ContextSimilarity(S t , C i ) is the context similarity between the context information S in the text information t And the i-th paragraph C in the text information i , λ 2 Is the second adjustment factor of the context function, δ 3 Is the third adjustment factor of the context function, κ 3 Is the fourth adjustment factor of the context function, TemporalEffect(C i ) is the temporal effect of the i-th paragraph C in the text information i At different reading stages.

[0096] Generate a knowledge graph module, used to generate a knowledge graph of the college English textbook according to the element information of each element, the relationship between each element and other elements, the importance score of each element, and the context of the context information, and conduct teaching according to the knowledge graph of the college English textbook.

[0097] Specifically, generating a knowledge graph of the college English textbook includes:

[0098]

[0099] Among them, Is the finally generated knowledge graph, n is the number of elements, Ω(E i ) is the i-th element E in the text informationi Element information extraction function, where the elements include: words, phrases, sentences, and / or paragraphs, Ω(R i ) is the relationship R between the i-th element and other elements in the text information i Relationship extraction function, Ω(P i ) is the importance score P of the i-th element in the text information i Importance scoring function, Ω(S t ) is the context information S in the text information t Context function of the context.

[0100] Embodiment 3

[0101] The embodiment of the present invention also proposes a storage medium storing multiple instructions for implementing the above-mentioned intelligent assisted teaching method for college English based on artificial intelligence.

[0102] Optionally, in this embodiment, the above storage medium can be located in any computer terminal in the computer terminal group in the computer network, or in any mobile terminal in the mobile terminal group.

[0103] Optionally, in this embodiment, the storage medium is set to store program codes for executing the following steps: Step 101, extract the text information in the college English textbook, and set an element information extraction function for extracting the element information of each element in the text information;

[0104] Specifically, the element information extraction function Ω(E i ) of the i-th element E in the text information includes: i

[0105]

[0106] Among them, α 1 is the first adjustment factor of the element information extraction function, Dist(E i , S t ) is the distance between the i-th element E i in the text information and the context information S t in the text information, β 1 is the second adjustment factor of the element information extraction function, γ 1 is the third adjustment factor of the element information extraction function, γ 2 is the fourth adjustment factor of the element information extraction function, δ 1 is the fifth adjustment factor of the element information extraction function, and A t is the structural information of the text information.

[0107] ​Step 102: Set up a relationship extraction function to extract the relationships between each element of the text information and other elements;

[0108] Specifically, the relationship R between the i-th element and other elements in the text information i of the relationship extraction function Ω(R i ) includes:

[0109]

[0110] where α 2 is the first adjustment factor of the relationship extraction function, Sim(E i , E j ) is the semantic similarity between the i-th element E i in the text information and the j-th element E j in the text information, β 2 is the second adjustment factor of the relationship extraction function, δ 2 is the third adjustment factor of the relationship extraction function, κ 1 is the fourth adjustment factor of the relationship extraction function, Sentiment(E i , E j ) is the sentiment analysis metric between the i-th element E i in the text information and the j-th element E j in the text information, used to capture the sentiment tendency between the i-th element E i and the j-th element E j in the text information.

[0111] Step 103: Set up an importance scoring function to extract the importance score of each element in the text information;

[0112] Specifically, the importance score P of the i-th element in the text information i of the importance scoring function Ω(P i ) includes:

[0113]

[0114] where α 3 is the first adjustment factor of the importance scoring function, β 3 is the second adjustment factor of the importance scoring function, Frequency(E i ) is the frequency of the i-th element E i appearing in the text information, γ 2 is the third adjustment factor of the importance scoring function, κ 2 is the fourth adjustment factor of the importance scoring function, ValueScore(E i ) is the i-th element E in the text informationi Value score, which is used to perform a value score on the i-th element in the text information based on the structure and key chapters of the college English textbook.

[0115] Step 104: Set a context function for extracting the context of the context information in the text information.

[0116] Specifically, the context information S in the text information t The context function Ω(S t ) includes:

[0117]

[0118] where m is the number of paragraphs, λ 1 is the first adjustment factor of the context function, ContextSimilarty(S t , C i ) is the context similarity between the context information S in the text information t and the i-th paragraph C in the text information i , λ 2 is the second adjustment factor of the context function, δ 3 is the third adjustment factor of the context function, κ 3 is the fourth adjustment factor of the context function, TemporalEffect(C i ) is the temporal effect of the i-th paragraph C in the text information i at different reading stages.

[0119] Step 105: Generate a knowledge graph of the college English textbook based on the element information of each element, the relationship between each element and other elements, the importance score of each element, and the context of the context information, and conduct teaching based on the knowledge graph of the college English textbook.

[0120] Specifically, generating the knowledge graph of the college English textbook includes:

[0121]

[0122] where is the finally generated knowledge graph, n is the number of elements, Ω(E i ) is the element information extraction function of the i-th element E in the text information i , and the elements include: words, phrases, sentences, and / or paragraphs, Ω(R i ) is the relationship extraction function of the relationship R between the i-th element and other elements in the text information i , Ω(P i ) is the importance score P of the i-th element in the text informationi importance scoring function, Ω(S t ) is the context function S of the context information in the text information t of the context.

[0123] Example 4

[0124] An embodiment of the present invention also provides an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the described intelligent assisted teaching method for college English based on artificial intelligence.

[0125] Specifically, the electronic equipment of this embodiment can be a computer terminal, and the computer terminal can include: one or more processors and a storage medium.

[0126] Among them, the storage medium can be used to store software programs and modules, such as an intelligent assisted teaching method for college English based on artificial intelligence in the embodiment of the present invention, the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizes the described intelligent assisted teaching method for college English based on artificial intelligence. The storage medium can include a high-speed random storage medium, and can also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium can further include a storage medium remotely set relative to the processor, and these remote storage media can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network and their combinations.

[0127] The processor can call the information and application programs stored in the storage medium through the transmission system to execute the following steps: Step 101, extract the text information in the college English textbook, and set an element information extraction function for extracting the element information of each element in the text information;

[0128] Specifically, the i-th element E in the text information i of the element information extraction function Ω(E i ) includes:

[0129]

[0130] Among them, α 1 is the first adjustment factor of the element information extraction function, Dist(E i , S t ) is the i-th element E in the text information i and the context information S in the text information tThe distance, β 1 Is the second adjustment factor of the element information extraction function, γ 1 Is the third adjustment factor of the element information extraction function, γ 2 Is the fourth adjustment factor of the element information extraction function, δ 1 Is the fifth adjustment factor of the element information extraction function, A t Is the structural information of the text information.

[0131] Step 102, set a relationship extraction function for extracting the relationship between each element of the text information and other elements;

[0132] Specifically, the relationship R between the i-th element and other elements in the text information i The relationship extraction function Ω(R i ) includes:

[0133]

[0134] Among them, α 2 Is the first adjustment factor of the relationship extraction function, Sim(E i , E j ) is the semantic similarity between the i-th element E i And the j-th element E in the text information j , β 2 Is the second adjustment factor of the relationship extraction function, δ 2 Is the third adjustment factor of the relationship extraction function, κ 1 Is the fourth adjustment factor of the relationship extraction function, Sentiment(E i , E j ) is the sentiment analysis metric between the i-th element E i And the j-th element E in the text information j , used to capture the sentiment tendency between the i-th element E i And the j-th element E in the text information j .

[0135] Step 103, set an importance scoring function for extracting the importance score of each element in the text information;

[0136] Specifically, the importance score P of the i-th element in the text information i The importance scoring function Ω(P i ) includes:

[0137]

[0138] Among them, α 3 Is the first adjustment factor of the importance scoring function, β3 is the second adjustment factor of the importance scoring function, Frequency(E i ) is the frequency of the i-th element E i appearing in the text information, γ 2 is the third adjustment factor of the importance scoring function, κ 2 is the fourth adjustment factor of the importance scoring function, ValueScore(E i ) is the value score of the i-th element E i in the text information, which is used to score the value of the i-th element in the text information based on the structure and key chapters of the college English textbook.

[0139] Step 104, set a context function for extracting the context of the context information in the text information;

[0140] Specifically, the context function Ω(S t ) of the context information S t ) in the text information includes:

[0141]

[0142] where m is the number of paragraphs, λ 1 is the first adjustment factor of the context function, ContextSimilarity(S t , C i ) is the context similarity between the context information S t in the text information and the i-th paragraph C i in the text information, λ 2 is the second adjustment factor of the context function, δ 3 is the third adjustment factor of the context function, κ 3 is the fourth adjustment factor of the context function, TemporalEffect(C i ) is the temporal effect of the i-th paragraph C i in the text information at different reading stages.

[0143] Step 105, generate a knowledge graph of the college English textbook according to the element information of each element, the relationship between each element and other elements, the importance score of each element, and the context of the context information, and conduct teaching according to the knowledge graph of the college English textbook.

[0144] Specifically, generating the knowledge graph of the college English textbook includes:

[0145]

[0146] where For the finally generated knowledge graph, n is the number of elements, and Ω(E i ) is the element information extraction function of the i-th element E in the text information. The elements include: words, phrases, sentences, and / or paragraphs. Ω(E i ) is the relationship extraction function of the relationship R between the i-th element and other elements in the text information. Ω(P i ) is the importance scoring function of the importance score P of the i-th element in the text information. Ω(S i ) is the context function of the context information S in the text information. Ω(S i ) is the importance scoring function of the importance score P of the i-th element in the text information. Ω(S i ) is the context function of the context information S in the text information. Ω(S t ) is the context function of the context information S in the text information. Ω(S t ) is the context function of the context information S in the text information.

[0147] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0148] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0149] In several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces. The indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0150] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, the functional units in the various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0152] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, read-only storage media (ROM, Read-Only Memory), random access storage media (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0153] Obviously, the above embodiments are merely examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or variations derived therefrom still fall within the protection scope of the present invention.

Claims

1. An intelligent-assisted teaching method for college English based on artificial intelligence, characterized in that: include: Extracting text information from a college English textbook, and setting an element information extraction function to extract element information of each element in the text information; Setting a relationship extraction function to extract the relationship between each element of the text information and other elements; Setting an importance scoring function to extract the importance score of each element in the text information; Setting a context function for extracting the context of the context information in the text information; A knowledge graph of a college English textbook is generated according to the element information of each element, the relationship between each element and other elements, the importance score of each element and the contextual context of the context information, and teaching is performed according to the knowledge graph of the college English textbook.

2. A method for intelligent-assisted teaching of college English based on artificial intelligence as claimed in claim 1, characterized in that: The knowledge graph for generating college English textbooks includes: in, is the final generated knowledge graph, n is the number of elements, Ω(E i ) is the i-th element E in the text information i The element information extraction function includes: words, phrases, sentences and / or paragraphs, Ω(R i ) is the relationship R between the i-th element and other elements in the text information i The relationship extraction function, Ω(P i ) is the importance score P of the i-th element in the text information i The importance scoring function, Ω(S t ) is the context information S in the text information t The context function.

3. A method for intelligent-assisted teaching of college English based on artificial intelligence as claimed in claim 2, characterized in that: The i-th element E in the text information i The element information extraction function Ω(E i )include: Among them, α1 is the first adjustment factor of the element information extraction function, Dist(E i , S t ) is the i-th element E in the text information i and context information S in text information t , β1 is the second adjustment factor of the element information extraction function, γ1 is the third adjustment factor of the element information extraction function, γ2 is the fourth adjustment factor of the element information extraction function, δ1 is the fifth adjustment factor of the element information extraction function, A t It is the structural information of text information.

4. A method for intelligent-assisted teaching of college English based on artificial intelligence as claimed in claim 3, characterized in that: The relationship R between the i-th element and other elements in the text information i The relation extraction function Ω(R i )include: Among them, α2 is the first adjustment factor of the relationship extraction function, Sim(E i , E j ) is the i-th element E in the text information i and the jth element E in the text information j The semantic similarity of the relation extraction function, β2 is the second adjustment factor of the relation extraction function, δ2 is the third adjustment factor of the relation extraction function, κ1 is the fourth adjustment factor of the relation extraction function, Sentiment (E i , E j ) is the i-th element E in the text information i and the jth element E in the text information j The sentiment analysis metric is used to capture the i-th element E in the text information. i and the jth element E in the text information j The emotional tendencies between them.

5. A method for intelligent-assisted teaching of college English based on artificial intelligence as claimed in claim 4, characterized in that: The importance score P of the i-th element in the text information i The importance score function Ω(P i )include: Among them, α3 is the first adjustment factor of the importance scoring function, β3 is the second adjustment factor of the importance scoring function, and Frequency(E i ) is the i-th element E in the text information i The frequency of occurrence in the text information, γ2 is the third adjustment factor of the importance scoring function, κ2 is the fourth adjustment factor of the importance scoring function, ValueScore (E i ) is the i-th element E in the text information i The value scoring is used to score the value of the i-th element in the text information based on the structure and key chapters of the college English textbook.

6. A method for intelligent-assisted college English teaching based on artificial intelligence as claimed in claim 5, characterized in that: Context information S in text information t The context function Ω(S t )include: Where m is the number of paragraphs, λ1 is the first adjustment factor of the context function, ContextSimilarity(S t , C i ) is the context information S in the text information t and the i-th paragraph C in the text information i , λ2 is the second adjustment factor of the context function, δ3 is the third adjustment factor of the context function, κ3 is the fourth adjustment factor of the context function, TemporalEffect(C i ) is the i-th paragraph C in the text information i Timing effects at different stages of reading.

7. An artificial intelligence-based intelligent assisted teaching system for college English, characterized in that: include: An element information extraction module is used to extract text information from a college English textbook and set an element information extraction function to extract element information of each element in the text information; A relationship extraction module, used for setting a relationship extraction function, used for extracting the relationship between each element of the text information and other elements; An importance scoring extraction module is used to set an importance scoring function to extract the importance score of each element in the text information; A context extraction module, used for setting a context function, used for extracting the context of the context information in the text information; A knowledge graph generation module is used to generate a knowledge graph of a college English textbook based on the element information of each element, the relationship between each element and other elements, the importance score of each element and the contextual context of the context information, and to teach according to the knowledge graph of the college English textbook.

8. A college English intelligent assisted teaching system based on artificial intelligence as claimed in claim 7, characterized in that: The knowledge graph for generating college English textbooks includes: in, is the final generated knowledge graph, n is the number of elements, Ω(E i ) is the i-th element E in the text information i The element information extraction function includes: words, phrases, sentences and / or paragraphs, Ω(R i ) is the relationship R between the i-th element and other elements in the text information i The relationship extraction function, Ω(P i ) is the importance score P of the i-th element in the text information i The importance scoring function, Ω(S t ) is the context information S in the text information t The context function.

9. A college English intelligent assisted teaching system based on artificial intelligence as claimed in claim 8, characterized in that: The i-th element E in the text information i The element information extraction function Ω(E i )include: Among them, α1 is the first adjustment factor of the element information extraction function, Dist(E i , S t ) is the i-th element E in the text information i and context information S in text information t , β1 is the second adjustment factor of the element information extraction function, γ1 is the third adjustment factor of the element information extraction function, γ2 is the fourth adjustment factor of the element information extraction function, δ1 is the fifth adjustment factor of the element information extraction function, A t It is the structural information of text information.

10. A college English intelligent assisted teaching system based on artificial intelligence as claimed in claim 9, characterized in that: The relationship R between the i-th element and other elements in the text information i The relation extraction function Ω(R i )include: Among them, α2 is the first adjustment factor of the relationship extraction function, Sim(E i , E j ) is the i-th element E in the text information i and the jth element E in the text information j The semantic similarity of the relation extraction function, β2 is the second adjustment factor of the relation extraction function, δ2 is the third adjustment factor of the relation extraction function, κ1 is the fourth adjustment factor of the relation extraction function, Sentiment (E i , E j ) is the i-th element E in the text information i and the jth element E in the text information j The sentiment analysis metric is used to capture the i-th element E in the text information. i and the jth element E in the text information j The emotional tendencies between them.

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