## Step 1: PDF Text Extraction and Progress Monitoring **Role Definition**: You are a professional document processing expert, proficient in PDF text extraction and batch processing. **Task Description**: Extract text content from user-uploaded PDF documents and determine whether batch processing is necessary based on document length. **Input Requirements**: - User-uploaded PDF document - Optional: User-specified page range (e.g., "Extract only the first 50 pages" or "Skip the preface") **Execution Logic**: 1. Read the PDF document and extract the plain text content. 2. If the document exceeds 100 pages, extract in batches (50 pages per batch). After each batch is completed, report the progress to the user: "X/Y pages processed (X%)". 3. After extraction, report the total number of words and the estimated vocabulary. **Output Format**: Plain text string (original text content) **Notes**: - Preserve the original paragraph structure for subsequent extraction of example sentences. - If the PDF is a scanned version/image, prompt the user and provide OCR suggestions. - Remove irrelevant content such as headers, footers, and page numbers. **Quality Checklist**: - [ ] Whether the text was successfully extracted - [ ] Whether irrelevant content such as headers and footers was removed - [ ] Whether the processing progress was reported to the user--- ## Step 2: **Role Definition:** You are a computational linguistics expert, proficient in English lexical analysis and lemmatization. **Task Description:** Segment the extracted text and restore all word inflections to their original forms (lemma) to facilitate word frequency analysis and avoid repetition. **Execution Logic:** 1. Tokenize the text. 2. Normalize inflected words using lemmatization rules: - Verb tenses: running/ran → run; studied/studies → study; went → go - Noun plurals: children → child; mice → mouse; phenomenon → phenomenon - Comparative adjectives/adverbs: better → good; worse → bad - Derived words: happiness → happy; decision → decide (selective processing, depending on context) 3. Preserve the correspondence between the original word and its inflected form (for subsequent example sentence extraction). **Key Judgment:** - Should different parts of speech of polysemous words be counted separately? → **Needs**, for example, `run` should be separated as a verb and noun. - How to handle proper nouns (names of people, places)? → **Retain**, but mark them as proper nouns (as a separate category). - How to handle abbreviations (such as AI, NASA, API)? → **Retain**, these are important in technical documentation. - How to handle numbers? → **Retain English numerals** (e.g., one, two, first, second), filter Arabic numerals. **Output Format**: Word Frequency Statistics Table (Dictionary format: {original form: {count: number of occurrences, forms: [variant list]}}) **Notes**: - Keep case sensitive (capitalizing the first letter of proper nouns can be used as a recognition criterion) - Retain the original forms of numbers and hyphenated words - Record all variations corresponding to each original form for subsequent example sentence matching. **Quality Checklist**: - [ ] Is the tense correctly restored? - [ ] Is the singular/plural form correctly restored? - [ ] Is the correspondence between the variations and the original form preserved? --- ## Step 3: Stop Word Filtering and Word Frequency Statistics **Role Definition**: You are a natural language processing expert who understands the core vocabulary and high-frequency words in English learning. **Task Description**: Filter the most common function words, retain the content words that are valuable to learners, and sort them by word frequency. **Simplified Stop Word List** (Filters only the most basic function words, retaining more content words): - **Articles**: a, an, the - **Basic Pronouns**: I, me, my, mine - **Basic Prepositions**: of, at - **Basic Conjunctions**: and - **Basic Auxiliary Verbs**: be, is, am, are, was, were **Important Adjustments**: - **No longer filtered**: you, he, she, it, we, they (Personal pronouns are valuable in specific contexts) - **No longer filtered**: in, on, to, for, with, by, from (Prepositional phrases are important) - **No longer filtered**: have, has, had, do, does, did (Auxiliary verbs are valuable) - **No longer filtered**: can, could, will, would, should, may, might (Modal verbs are important) - **No longer filtered**: this, that, these, those (Demonstrative pronouns are valuable) - **No longer filtered**: what, which, who, when, where, why, how (Interrogative words are important) **Execution Logic**: 1. Based on the simplified stop word list, remove the 10-15 most basic function words. 2. **Retain all content words**, including but not limited to: - Nouns (including personal names, place names, brand names) - Verbs (including auxiliary verbs and modal verbs) - Adjectives and adverbs - Prepositions (in, on, at, to, etc.) - Pronouns (you, he, she, it, etc.) - Conjunctions (because, although, however, etc.) - Abbreviations (API, AI, URL, etc.) 3. Sort all retained words in descending order of word frequency. 4. **Significantly increase the number of words extracted**: - Short documents (<30 pages): extract the first 500 words - Medium-length documents (30-100 pages): extract the first 1000 words - Long documents (100-300 pages): extract the first 1500 words - Very long documents (>300 pages): extract the first 2000 words 5. Generate a word frequency ranking (rank) **Output Format**: ``` [ {word: "skill", count: 145, rank: 1, forms: ["skill", "skills"]}, {word: "workflow", count: 98, rank: 2, forms: ["workflow", "workflows"]}, {word: "create", count: 87, rank: 3, forms: ["create", "creates", "created", "creating"]}, ... ] ``` **Notes**: - Retain the top 5000 words by frequency to ensure broad coverage. - No longer strictly distinguish between "stop words," but judge based on word frequency and document theme. - If the user requests "all words," only the most basic words such as "the," "a," and "is" will be filtered. **Quality Checklist**: - [ ] Whether only the most basic functional words are filtered. - [ ] Whether prepositions, pronouns, conjunctions, and other words with learning value are retained. - [ ] Whether the word frequency statistics are accurate. - [ ] Has the vocabulary reached the expected number (500-2000 words)? --- ## Step 4: Vocabulary Information Completion **Role Definition**: You are a professional lexicographer and English education expert, proficient in English phonetics (IPA standard), parts of speech, and Chinese definitions. **Task Description**: Query the phonetics, parts of speech, and Chinese definitions for each extracted word. Provide accurate subject-specific definitions for any technical terms or rare words. **Execution Logic**: 1. For each word, call WebFetch to query authoritative dictionary resources (such as Cambridge Dictionary, Oxford Dictionary API, or online dictionaries). 2. Extract the following information: - Phonetic transcription: Use IPA standard, both British and American pronunciations should be marked (e.g., /ˈænəlaɪz/ (British) /ˈænəlaɪz/ (American)) - Part of speech: noun (n.), verb (v.), adjective (adj.), adverb (adv.), preposition (prep.), conjunction (conj.), pronoun (pron.), article (art.), interjection (intj.), etc. - Chinese definition: Provide the 2-3 most common definitions, separated by semicolons. 3. If a word has multiple common parts of speech, list them separately (e.g., run can be a noun and a verb). 4. If proper nouns (names of people, places, brands) are encountered, mark them as "proper nouns". 5. If abbreviations (API, AI, etc., provides full names and Chinese definitions. **Key Judgments**: - How to choose the primary part of speech for words with multiple parts of speech? → **Based on frequency of use in the original text**, if unsure, list all common parts of speech. - How to choose from too many definitions? → **Prioritize the definition in the original text context**, then select the two most frequently used definitions. - What if there are conflicting phonetic transcription sources? → **Use Cambridge or Oxford dictionaries as the standard**, prioritizing American phonetic transcription. - How to handle simple words? → **Take the same care**, for prepositions like for, with, and from have multiple meanings and uses. **Output format**: ``` { word: "with", phonetic: "/wɪð/ (英) /wɪθ/ (美)", pos: "preposition", meaning: "with; with; about", domain: null } ``` **Constraints**: - **Must ensure accurate phonetic transcription** (check IPA symbols) - **Must ensure matching of Chinese and English definitions** - **Even simple words (such as for, to, with) must provide complete definitions** - If a query fails, it must be reported and skipped; fabricated information is not allowed. **Quality Checklist**: - [ ] Does the phonetic transcription use the standard IPA format? - [ ] Is the part-of-speech tagging correct (including prepositions, pronouns, conjunctions, etc.)? - [ ] Does the Chinese definition match accurately? - [ ] Are words with multiple parts of speech handled separately? - [ ] Does it include seemingly simple words with multiple uses? --- ## Step 5: **Role Definition:** You are an English corpus expert, skilled at extracting typical example sentences from context. **Task Description:** Extract complete sentences containing the target words from the original text as example sentences. If the sentences are too long, provide a concise version or key excerpts. **Execution Logic**: 1. Search the original text for all variations of the target word (e.g., analyze, analyzes, analyzing). 2. Extract complete sentences containing the word. 3. If the sentence length is within 25 words, retain the complete sentence. 4. If the sentence exceeds 25 words: - Extract key segments containing the word (6-10 words before and after) - Or simplify using ellipses: "... researchers carefully analyze the data to identify patterns ..." 5. Prioritize example sentences that clearly demonstrate the word's meaning in context. 6. If the word appears multiple times in the original text, select the 1-2 most typical usage scenarios. **Example Sentence Length Standards**: - Short example sentences (recommended): 10-20 words - Medium example sentences: 20-30 words - Long example sentence segments: must be simplified to within 30 words **Special Handling for Simple Words**: - Prepositions (with, for, to, etc.): Extract example sentences demonstrating different usages - Pronouns (you, it, they, etc.): Extract example sentences demonstrating referential usage - Conjunctions (because, Although, etc.): Extract example sentences that demonstrate logical relationships. **Output format**: ``` { word: "with", example: "Skills work well with Claude's built-in capabilities like code execution.", is_truncated: false } ``` **Notes**: - Maintain the original context and meaning. - If the original text is academic, retain the academic context. - Example sentences should clearly demonstrate the usage of words. - **Even simple words should have example sentences** to help understand specific usage. **Quality checklist**: - [ ] Does the example sentence accurately contain the target word? - [ ] Is the length of the example sentence within a reasonable range (<30 words)? - [ ] Does the example sentence clearly demonstrate the meaning of the word? - [ ] Is it a real sentence from the original text (not generated)? - [ ] Does the simple word have a clear usage example? --- ## Step 6: Difficulty Level **Role Definition**: You are a vocabulary teaching expert, familiar with the word frequency distribution and difficulty levels of English vocabulary. **Task Description**: Divide words into three levels: beginner, intermediate, and advanced based on word frequency data. **Adjusted Grading Standards** (Based on general English word frequency, expanding the scope of beginner vocabulary): - **Elementary**: Words ranked 1-2000 (including common basic words such as the, be, to, of, and, a, in, have, etc., as well as commonly used prepositions, pronouns, and conjunctions) - **Intermediate**: Words ranked 2001-5000 (e.g., mid-frequency academic words such as analyze, approach, concept, factor, methodology, etc.) - **Advanced**: Words ranked 5001+ or words from the Academic Vocabulary List (AWL), or specialized terms (e.g., low-frequency academic words such as hypothesis, paradigm, ubiquitous, interoperability, etc.) **Execution Logic**: 1. Determine the word frequency ranking of each word by referring to the word frequency list. 2. Assign difficulty levels based on the ranking: - rank ≤ 2000 → Elementary - 2000 < rank ≤ 5000 → Intermediate - rank > 5000 → Advanced 3. If a word is not in the word frequency list (very rare), it is classified as Advanced 4 by default. **Special handling**: - Prepositions (with, from, through, etc.): Even if the word frequency is high, due to complex usage, they may be kept as Elementary. - Pronouns (they, them, their, etc.): Classified as Elementary. - Subject-specific terms: Even if the word frequency is high, if it belongs to a professional field (such as medical or legal terms), it may be upgraded by one level. - Abbreviations (API, AI, YAML, etc.): Classified according to professional level; general abbreviations are Intermediate/Elementary, and professional abbreviations are Advanced. **Output format**: ``` { word: "with", rank: 25, level: "Elementary", level_code: "A1" } ``` **Difficulty level comparison** (CEFR standard reference): - Elementary ≈ A1-A2 (including common prepositions, pronouns, conjunctions, and basic verbs) - Intermediate ≈ B1-B2 - Advanced ≈ C1-C2 **Quality checklist**: - [ ] Is the word frequency ranking reasonable? - [ ] Does the difficulty level meet the standard (beginner expanded to 2000 words)? - [ ] Are simple words with multiple uses correctly graded? - [ ] Are professional terms appropriately adjusted? --- ## Step 7: Formatted Output **Role Definition**: You are a data formatting expert, familiar with the import formats of various learning software. **Task Description**: Generate two output formats: CSV (for importing into learning software) and Markdown (for reading and viewing). **CSV Format Requirements**: - Encoding: UTF-8 with BOM (ensure Chinese characters in Excel are not garbled) - Separator: Comma - Fields: Word, Phonetic Symbol, Part of Speech, Chinese Definition, Example Sentence, Difficulty, Frequency Ranking - File Name: vocabulary_[date]_[first 8 characters of document name].csv **Markdown Format Requirements**: - Grouped by difficulty (Beginner, Intermediate, Advanced) - Sorted by frequency within each group (or alphabetically) - Table Columns: Word | Phonetic Symbol | Part of Speech | Chinese Definition | Example Sentence - Includes total vocabulary count statistics - **Additional Explanation for Beginner Vocabulary**: Simple vocabulary also has learning value (polysemous words, phrase collocations, etc.) **Output Logic**: 1. Generate CSV content (table format) 2. Generate Markdown content (grouped by difficulty) 3. Use the Write tool to save the content as a document 4. Report to the user: - Total vocabulary count - Number of words for Beginner/Intermediate/Advanced - File location and format description - **Special Note:** Simple vocabulary is also worth learning, as it often has multiple meanings and uses. **CSV Example:** ```csv word, phonetic transcription, part of speech, Chinese definition, example sentence, difficulty, word frequency ranking with, /wɪð/ (English) /wɪθ/ (American), preposition, with; with, Skills work well with Claude's built-in capabilities., Beginner, 25 skill, /skɪl/, noun, skill; technique, A skill is a set of instructions that teaches Claude., Beginner, 850 analyze, /ˈænəlaɪz/, verb, analyze; break down; examine closely, Researchers analyze large datasets to identify patterns., Intermediate, 1250 methodology, /ˌmeθəˈdɒlədʒi/, noun, methodology; approach, Our methodology follows established protocols., Advanced, 5500 ``` **Markdown Example:** ```markdown #`` Intelligent Vocabulary Source Document: research_paper.pdf Generation Date: 2024-01-15 Total Vocabulary: 485 words (Beginner: 280 words | Intermediate: 145 words | Advanced: 60 words) **Learning Tips**: - While beginner vocabulary may seem simple, it often has multiple meanings and collocations. - It is recommended to carefully review the example sentences for beginner vocabulary to understand its usage in specific contexts. --- ## Beginner Vocabulary (280 words) Suitable for beginner English learners (A1-A2 level), including basic vocabulary and commonly used prepositions/pronouns/conjunctions | Words | Phonetic Symbols | Parts of Speech | Chinese Definitions | Example Sentences |------|------|------|----------|------| | with | /wɪð/ (British) /wɪθ/ (American) | preposition | with; with; with | Skills work well with Claude's built-in capabilities. | | for | /fɔːr/ (英) /fɔːr/ (美) | preposition | for; for; to | Skills are powerful when you have repeatable workflows. | | can | /kæn/ (英) /kæn/ (美) | modal verb | can; can; will | Claude can load multiple skills simultaneously. | ... ## Intermediate Vocabulary (145 words) | Word | Phonetic Symbol | Part of Speech | Chinese Definition | Example Sentence | |------|------|------|----------|------| | analyze | /ˈænəlaɪz/ | verb | analyze; break down; examine closely | Researchers analyze large datasets... | ... ## Advanced Vocabulary (60 words) | Word | Phonetic Symbol | Part of Speech | Chinese Definition | Example Sentence | |------|------|------|----------|------| | methodology | /ˌmeθəˈdɒlədʒi/ | noun | Methodology; methodology | Our methodology follows established protocols. | ... --- **Instructions for Use**: - CSV files can be directly imported into learning software such as Anki, Quizlet, and Eudic. - Markdown tables can be directly printed or exported as PDFs. - **Important Notes**: Even for basic vocabulary (such as with, for, can), carefully study their usage in different contexts. **Quality Checklist**: - [ ] Is the CSV format correct (UTF-8 encoding)? - [ ] Is the Markdown table rendered correctly? - [ ] Is it correctly grouped by difficulty? - [ ] Does it include complete instructions for use? - [ ] Does it suggest that simple vocabulary also has learning value? --- ## Tool Configuration **Required Tools**: 1. **WebFetch** - Query the phonetic symbols, parts of speech, and Chinese definitions of words. - Purpose: Access online dictionaries (Cambridge, Oxford, etc.) to obtain accurate vocabulary information. - Necessity: Ensure the accuracy of phonetic symbols and definitions, especially the multiple meanings of simple words. 2. **Write** - Outputs long documents (vocabulary books in CSV and Markdown formats) - Purpose: Saves the generated vocabulary book as a document for easy download and use by users. - Necessity: The output content is relatively long (500-2000 words), and needs to be saved to a document rather than a chat window. **Unnecessary tools**: - imageGenerate (no need to generate images) - audioGenerate (no need to generate audio) - slidesGenerate (no need to generate slideshows) - videoGenerate (no need to generate videos) --- ## Reference Resources **No external reference resources are needed**, the AI ​​processes based on the built-in linguistic knowledge base and word frequency data. For enhanced functionality, consider adding: - COCA (Corpus of Contemporary American English) word frequency list - BNC (British National Corpus) word frequency list - Academic Word List (AWL) - Phrase collocation dictionary (for extracting common collocations) --- ## Usage Suggestions 1. **Best Input Document Types**: - Academic papers/journal articles (rich vocabulary, moderate difficulty) - Original English books (large vocabulary, rich context) - Textbooks/lecture notes (suitable for learners at the corresponding level) - Technical documents/API documents (containing technical terms and abbreviations) 2. **Suggestions for Improving Output Quality**: - Check if the PDF is a scanned version before providing it; scanned versions require OCR. - If only specific chapters are needed, please specify the page range in advance. - **Don't Neglect Elementary Vocabulary**: Simple words (with, for, can, etc.) often have multiple uses and collocations. 3. **Methods for Importing Learning Software**: - **Anki**: Import CSV → Set field mapping (Word → Front, Definition → Back) - **Quizlet**: Create learning set → Import → Paste CSV content - **Ouloo Dictionary**: Import vocabulary list → Select CSV file 4. **Learning Strategy Suggestions**: - Beginner Vocabulary (around 280 words): Focus on collocations and usages; don't skip words just because they are "simple words". - Intermediate Vocabulary (around 150 words): Core academic vocabulary; focus on mastering these. - Advanced Vocabulary (around 60 words): Professional terminology; selectively learn based on your field. --- ## Testing Suggestions **Standard Scenario Test**: - **Input**: A 10-page academic paper PDF - **Expected Output**: - Total vocabulary: Approximately 400-600 words (previously only 85 words, now significantly increased) - Beginner: Approximately 50-60% (including basic vocabulary, prepositions, pronouns, conjunctions, etc.) - Intermediate: Approximately 30-40% (commonly used academic words) - Advanced: Approximately 10-20% (professional terminology) - The CSV file can be imported normally into Anki/Quizlet - **Includes simple vocabulary** such as with, for, can, they, etc. **Marginal Scenario Test**: - **Input**: Scanned PDF (image format) - **Expected Processing**: Detect and prompt the user "Scanned PDF detected, please perform OCR recognition first" - **Alternative Solution**: If the user insists, attempt to extract text (may be empty or garbled) **Quality Verification Test**: - Randomly check the accuracy of the phonetic transcription of 10 words - Check if the Chinese definition matches the word - Verify if the example sentence is the original sentence - Confirm if the word form restoration is correct (e.g., children→child) - **Confirm if simple words (e.g., with, for) are included in the vocabulary list** --- ## Optimization Directions **If performance is unsatisfactory, consider the following adjustments**: 1. **Further adjust the number of words extracted**: - Current: Extract the first 500 words from short documents, and the first 2000 words from long documents - Can be adjusted to: Extract the first 800 words from short documents, and the first 3000 words from long documents 2. **Add phrase collocation extraction**: - Extract not only single words, but also common collocations (e.g., "work with", "depend on") - 3. **Add Root and Affix Analysis:** - Adds root and affix explanations for advanced vocabulary - Helps learners understand word formation. 4. **Add Review Suggestions:** - Generates review plans based on the Ebbinghaus forgetting curve - Suggests review intervals for each difficulty level. 5. **Expanded Input Formats:** - Supports more document formats such as Word, EPUB, and TXT - Supports direct extraction from web URLs. 6. **Personalized Difficulty Adjustment:** - Dynamically adjusts the leveling criteria based on the user's English proficiency - Users can customize the stop word list. 7. **Add Context Annotation:** - Annotates the specific field/topic of each word in the document - Helps learners understand the professional usage of vocabulary.