English vocabulary memorization method and system based on natural language processing

Abstract

Disclosed in the present invention are an English vocabulary memorization method and system based on natural language processing. The method comprises: collecting a large-scale English text corpus and preprocessing same, so as to obtain processed data; on the basis of the processed data, constructing a semantic association network between vocabulary items; on the basis of user conditions, using the semantic association network to plan a learning path; and on the basis of the learning path, completing English vocabulary memorization. In the present invention, by means of natural language processing technology, a semantic association network between vocabulary items is constructed and a personalized learning path is planned, thereby effectively solving the problems of dullness, inefficiency and the lack of pertinence of traditional English vocabulary memorization methods. A large amount of preprocessed corpus data is used in combination with a co-occurrence matrix or a word embedding model to accurately capture semantic relationships between the vocabulary items, such that learners can efficiently memorize the vocabulary items within associated context. The present invention provides an innovative and efficient technical solution for English vocabulary teaching, and has a significant practical value and broad promotion and application prospects.

Description

A method and system for memorizing English vocabulary based on natural language processing Technical Field

[0001] This invention relates to the field of educational technology, specifically to a method and system for memorizing English vocabulary based on natural language processing. Background Technology

[0002] Traditional methods of memorizing English vocabulary lack personalization, engagement, and efficiency, easily leading to boredom and poor retention. However, with the development of Natural Language Processing (NLP) technology, applying NLP techniques to English vocabulary memorization has become possible, improving memorization efficiency and enhancing learners' interest.

[0003] Existing NLP-based vocabulary memorization tools mostly focus on simple word meaning explanations and example sentences, failing to fully utilize the advantages of NLP technology for in-depth vocabulary association analysis, personalized learning path planning, and real-time feedback mechanisms. Therefore, there is an urgent need for an English vocabulary memorization method that comprehensively utilizes NLP technology to help learners master English vocabulary more efficiently. Summary of the Invention

[0004] To address the technical problems mentioned above, the present invention provides the following technical solution:

[0005] A method for memorizing English vocabulary based on natural language processing, comprising the following steps:

[0006] A large English text corpus was collected and preprocessed to obtain the processed data.

[0007] Based on the processed data, a semantic association network among words is constructed;

[0008] Based on user characteristics, a semantic association network is used to plan the learning path;

[0009] Based on the learning path, complete the memorization of English vocabulary.

[0010] Preferred methods for obtaining processed data include: collecting and organizing a large English text corpus, performing word segmentation, part-of-speech tagging, and named entity recognition on the text in the corpus, and forming a structured lexical information database.

[0011] Preferably, the method for constructing a semantic association network includes: using a co-occurrence matrix or word vector model to construct a semantic association network between words; and combining the context to analyze the frequency of word usage in different scenarios and its collocation relationship with other words, thereby further enriching the word association information.

[0012] Preferably, the co-occurrence matrix or word vector model includes: the Word2Vec model; the similarity between words is calculated by mapping words to a high-dimensional vector space and using the distance or similarity between these vectors to measure the semantic relationship between words.

[0013] Where v(w1) and v(w2) are the word vectors of words w1 and w2, respectively; || represents the modulus of the vector.

[0014] Preferred methods for planning learning paths include: comprehensively considering the vocabulary mastery of individuals, including both mastered and unlearned vocabulary; and learning objectives, such as short-term or long-term memory; and using semantic association networks to create learning plans based on these factors.

[0015] This invention also provides an English vocabulary memorization system based on natural language processing. The system implements the above method and includes: a data acquisition module, a construction module, a planning module, and an interaction module. The system's workflow includes:

[0016] The data acquisition module is used to collect a large amount of English text corpus and preprocess it to obtain processed data;

[0017] The building module is used to construct a semantic association network between words based on the processed data;

[0018] The interaction module is used to plan learning paths based on user circumstances and semantic association networks;

[0019] Users can use interactive modules to memorize English vocabulary based on learning paths.

[0020] Preferably, the workflow of the acquisition module includes: collecting and organizing a large amount of English text corpus, performing word segmentation, part-of-speech tagging, and named entity recognition on the text in the corpus, and forming a structured vocabulary information database.

[0021] Preferably, the workflow of the construction module includes: using a co-occurrence matrix or word vector model to construct a semantic association network between words; and combining the context to analyze the frequency of word usage in different scenarios and its collocation relationship with other words, thereby further enriching the word association information.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] This invention constructs a semantic association network for vocabulary using natural language processing technology and plans personalized learning paths, effectively solving the problems of tedium, inefficiency, and lack of specificity in traditional English vocabulary memorization methods. Utilizing pre-processed large-scale corpus data, combined with co-occurrence matrices or word vector models, it accurately captures the semantic relationships between words, enabling learners to efficiently memorize vocabulary in context. Simultaneously, it dynamically adjusts the learning path based on learners' vocabulary mastery and learning goals, not only improving memorization efficiency but also enhancing the interest and initiative in learning. This provides an innovative and efficient technical solution for English vocabulary teaching, possessing significant practical value and promising prospects for widespread application. Attached Figure Description

[0024] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] Example 1

[0029] Figure 1 shows a schematic diagram of the method flow of this embodiment, and the steps include:

[0030] S1. Collect a large amount of English text corpus and preprocess it to obtain processed data.

[0031] We collected and organized a large amount of English text corpus, and performed preprocessing operations such as word segmentation, part-of-speech tagging, and named entity recognition on the text in the corpus to form a structured lexical information database.

[0032] Specifically, the data collection channels include news articles, literary works, academic papers, and social media to ensure coverage of a wide range of language styles and fields. The collected raw texts are usually in unprocessed natural language form, therefore requiring a series of preprocessing operations for subsequent analysis and application.

[0033] First, the text in the corpus is tokenized, which involves dividing continuous text into words, punctuation marks, and other linguistic units. Next is part-of-speech tagging, which assigns each word its grammatical role in the sentence, such as noun, verb, or adjective. This process relies on context and language models to help better understand the structure and meaning of sentences.

[0034] Next is Named Entity Recognition (NER), used to identify and classify proper nouns in text, such as names of people, places, organizations, dates, and times. This helps extract key information from the text and lays the foundation for further semantic analysis.

[0035] These preprocessing steps transform the raw text into a structured lexical information base, where each word not only contains its basic form but also includes rich meta-information such as part of speech and entity category. This structured data provides a solid foundation for subsequent natural language processing tasks, such as machine translation, sentiment analysis, and question-answering systems.

[0036] S2. Based on the processed data, construct a semantic association network between words.

[0037] By utilizing co-occurrence matrices or word vector models (such as Word2Vec and GloVe), the similarity between words is calculated, and a semantic association network between words is constructed. Combined with contextual information, the frequency of word usage in different scenarios and its collocational relationships with other words are analyzed to further enrich the word association information.

[0038] To construct semantic association networks among words, co-occurrence matrices or word vector models (such as Word2Vec and GloVe) are first used to calculate the similarity between words. Co-occurrence matrices capture the co-occurrence relationships between words by statistically analyzing the frequency of their co-occurrence in the same context; while word vector models map words to vector representations in a high-dimensional space, ensuring that semantically similar words are close together in this space. These methods can effectively quantify the semantic similarity between words, thus providing a foundation for constructing word association networks.

[0039] Specifically, this embodiment uses Word2Vec to calculate the similarity between words. Calculating word similarity involves mapping words to a high-dimensional vector space and using the distance or similarity between these vectors to measure the semantic relationship between words. Specifically, the Word2Vec model is trained on a large amount of text data to generate a fixed-dimensional vector representation for each word, ensuring that semantically similar words are close in the vector space.

[0040] To calculate the similarity between two words w1 and w2, cosine similarity is typically used. Cosine similarity measures the degree of similarity between two vectors by calculating the cosine of the angle between them. The value of cosine similarity ranges from -1 to 1; a value closer to 1 indicates greater similarity, a value closer to -1 indicates less similarity, and 0 indicates orthogonality (i.e., irrelevant). The formula is as follows:

[0041] Where v(w1) and v(w2) are the word vectors of words w1 and w2, respectively; || represents the modulus of the vector (i.e., the Euclidean norm).

[0042] The similarity between words is quantified using the formula described above, thereby constructing a semantic association network between words. This vector space-based method can not only capture the static semantic information of words, but also reflect the dynamic changes of words in different contexts to a certain extent.

[0043] Next, considering the context, we analyze the frequency of word usage in different scenarios and its collocation with other words. Specifically, we examine the frequency of each word's occurrence in various contexts to understand its common usage scenarios and identify other words that frequently co-occur with it. For example, in scientific literature, "algorithm" often appears alongside words like "data" and "computation," while in literary works, "love" may be more often associated with words like "heart" and "passion." This context-based analysis not only enriches the semantic information of words but also reveals the multiple roles and dynamic changes of words in the network.

[0044] In this way, we can further enrich lexical association information, forming a more complex and refined semantic network. In this network, each word is no longer an isolated node, but is closely connected with other words through various semantic relationships. This rich association information can provide more accurate and comprehensive support for natural language processing tasks (such as text classification, information retrieval, machine translation, etc.), and also provides valuable resources for in-depth linguistic research.

[0045] S3. Based on user circumstances, a semantic association network is used to plan the learning path.

[0046] First, it's necessary to comprehensively consider the learner's vocabulary mastery (e.g., already mastered vocabulary and vocabulary to be learned) and learning objectives (e.g., short-term or long-term memory). Based on these factors, a semantic association network can be used to create an efficient and targeted learning plan.

[0047] Specifically, the recommended number of new words to learn each day should be determined based on the learner's current vocabulary level. For example, beginner learners might be advised to learn 5 to 10 new words per day, while advanced learners can appropriately increase this number according to their abilities. At the same time, following the memory patterns revealed by the Ebbinghaus forgetting curve, the time intervals for reviewing old vocabulary should be set to ensure that the words are firmly remembered. For short-term memory goals, more frequent review is needed, such as every other day or every few days; while for long-term memory goals, spaced repetition can be used, gradually extending the review intervals to optimize memory effectiveness.

[0048] S4. Based on the learning path, complete the memorization of English vocabulary.

[0049] The learning path will be dynamically adjusted according to the learner's actual progress. If the learner performs well at a certain stage, the system can speed up the introduction of new vocabulary and extend the review interval; conversely, if the learner encounters difficulties, the learning pace of new vocabulary will be slowed down and the review frequency will be increased to ensure that each word is fully mastered.

[0050] Example 2

[0051] This invention also provides an English vocabulary memorization system based on natural language processing, comprising: a data acquisition module, a construction module, a planning module, and an interaction module. The data acquisition module collects and preprocesses a large English text corpus to obtain processed data; the construction module constructs a semantic association network between words based on the processed data; the interaction module plans a learning path based on the user's situation and the semantic association network; and the user uses the interaction module to complete English vocabulary memorization based on the learning path.

[0052] The workflow of the data acquisition module includes: collecting and organizing a large amount of English text corpus, performing word segmentation, part-of-speech tagging, and named entity recognition on the text in the corpus, and forming a structured vocabulary information database.

[0053] The workflow of the construction module includes: using co-occurrence matrices or word vector models to build a semantic association network between words; and combining the context to analyze the frequency of word usage in different scenarios and its collocation with other words, thereby further enriching the word association information.

[0054] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A natural language processing-based English vocabulary memorization method, characterized by the steps of The application relates to an English vocabulary learning system and method. A large amount of English text corpus is collected and preprocessed to obtain processed data; Based on the processed data, a semantic association network between words is constructed; Based on the user situation, a learning path is planned using the semantic association network; Based on the learning path, English vocabulary memory is completed.

2. The natural language processing based English vocabulary memorizing method according to claim 1, characterized in that, The method for obtaining the processed data comprises the following steps: collecting and sorting a large amount of English text corpus, performing word segmentation, part-of-speech tagging and named entity recognition on the text in the corpus, and forming a structured vocabulary information library.

3. The natural language processing based English vocabulary memorizing method according to claim 1, characterized in that, The method for constructing the semantic association network comprises the following steps: constructing a semantic association network between words by using a co-occurrence matrix or a word vector model; combining the context, analyzing the use frequency of the words in different scenes and the collocation relationship between the words and other words, and further enriching the vocabulary association information.

4. The natural language processing based English vocabulary memorizing method according to claim 3, characterized in that, Co-occurrence matrix or word vector models include: Word2Vec models; calculating the similarity between words by Word2Vec models is by mapping words to high-dimensional vector space, and using the distance or similarity between these vectors to measure the semantic relationship between words: Wherein, v(w1) and v(w2) are word vectors of words w1 and w2 respectively; || represents the module of the vector.

5. The natural language processing based English vocabulary memorizing method according to claim 1, characterized in that, The method for planning the learning path comprises the following steps: comprehensively considering the vocabulary mastery of the personnel, including mastered vocabulary and to-be-studied vocabulary; and learning goals including short-term memory or long-term memory; based on these factors, a learning plan is made using the semantic association network.

6. A natural language processing based English vocabulary memorizing system, the system being configured to implement the method of any one of claims 1-5, characterized in that, The application relates to an English vocabulary learning system and method. The working process of the system comprises the following steps: The collection module is used for collecting a large amount of English text corpus and pre-processing to obtain processed data; The construction module is used for constructing a semantic association network between words based on the processed data; The interaction module is used for planning a learning path based on the user situation using the semantic association network; The user uses the interaction module to complete English vocabulary memory based on the learning path. The working process of the collection module comprises the following steps: collecting and sorting a large amount of English text corpus, performing word segmentation, part-of-speech tagging and named entity recognition on the text in the corpus, and forming a structured vocabulary information library.

7. The natural language processing based English vocabulary memorizing system according to claim 6, characterized in that, The working process of the construction module comprises the following steps: constructing a semantic association network between words by using a co-occurrence matrix or a word vector model; combining the context, analyzing the use frequency of the words in different scenes and the collocation relationship between the words and other words, and further enriching the vocabulary association information.

8. The natural language processing based English vocabulary memorizing system according to claim 6, characterized in that, ​

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