重复文本片段提取算法：高频重复内容提取、短键编码及学术术语咨询

Great question! Let's tackle this from both the algorithmic implementation side and the academic terminology perspective.

1. Yes, Effective Algorithms Exist for This Task #

Building a system that extracts high-frequency repeated units and maps them to unique shortcut keys (like Mac's Text Expansion) is totally feasible. Here's a step-by-step breakdown of the core workflow:

Step 1: Corpus Preprocessing #

First, clean and prepare your text data to ensure accurate extraction:

• Tokenize the text using language-specific tools (e.g., spaCy/NLTK for English, jieba for Chinese) to split text into words/subwords.
• Normalize the text: standardize case (lowercase for English), remove punctuation/special characters, filter stopwords (common low-information words like "the", "and"), and handle language-specific nuances (e.g., traditional/simplified Chinese conversion).

Step 2: Extract High-Frequency Units #

Depending on whether you're targeting single words, phrases, or word fragments, use these methods:

• Single words: Use simple frequency counting (e.g., collections.Counter in Python) to rank words by occurrence. Set a threshold (e.g., top 5% most frequent words) to pick candidates.
• Phrases/word fragments:
  • Use n-gram models (test n=2 to n=5) to count consecutive word sequences. For example, a 3-gram would capture "oh my god".
  • For more robust phrase mining, apply frequent itemset mining algorithms like Apriori or FP-Growth to identify repeated contiguous (or semi-contiguous) word groups that appear above a minimum support threshold.
  • For subword fragments (e.g., "un-" from "unhappy", "unfair"), use subword tokenization tools (like BPE) to identify repeated morphemes or common word chunks.

Step 3: Generate Unique Shortcut Keys #

The key here is ensuring real-time uniqueness while creating intuitive shortcuts:

• Uniqueness check: Maintain a hash map/dictionary that tracks existing shortcut-to-phrase mappings. Before assigning a new shortcut, verify it doesn't already exist.
• Shortcut generation rules:
  • Initialism: The most common approach (e.g., "oh my god" → "omg")—take the first letter of each word in the phrase.
  • Truncation: For long single words, use a shortened version (e.g., "unfortunately" → "unfort").
  • Custom rule-based generation: Combine key syllables or use user-preferred patterns (you can even train a small ML model to learn existing abbreviation patterns in your corpus).
• Conflict resolution: If a generated shortcut is already taken, adjust it—add a numeric suffix (e.g., "omg" → "omg1"), use an alternative rule, or pick a less common but still intuitive shortcut.

Step 4: Iterative Optimization #

• Incorporate user feedback: If users frequently override a shortcut, update your mapping or adjust generation rules to match their preferences.
• Dynamic ranking: Re-prioritize shortcuts based on actual usage frequency (e.g., make the most-used phrases have the shortest, easiest-to-type shortcuts).

2. Academic Terminology for This Field #

This task spans several subfields of computational linguistics and NLP, with these key terms:

• Phrase Mining: The core field focused on extracting meaningful, repeated phrases from text corpora—this covers the high-frequency unit extraction part of your question.
• Text Abbreviation Generation: A specific sub-task under text normalization, which focuses on converting long text units into concise, human-readable shortcuts.
• Lexical Simplification: While broader (it includes simplifying complex vocabulary to easier terms), some work in this area overlaps with abbreviation generation for accessibility and efficiency.
• Text Expansion Systems: The umbrella term for applications like Mac's Text Expansion—your system is essentially a reverse pipeline for building these systems (generating shortcuts from frequent phrases, rather than expanding shortcuts to phrases).

内容的提问来源于stack exchange，提问作者rudnev