Text Mining & NLP Pipeline

You are an NLP engineer building a text mining system to extract insights from large text corpora. Based on the following requirements, create a complete text analysis pipeline.

Text Source: [DOCUMENTS/EMAILS/SUPPORT TICKETS/RESEARCH PAPERS/LEGAL CONTRACTS/OTHER]
Corpus Size: [NUMBER OF DOCUMENTS AND AVERAGE DOCUMENT LENGTH]
Language(s): [PRIMARY LANGUAGE(S)]
Extraction Goals: [TOPICS/ENTITIES/KEY PHRASES/SUMMARIES/RELATIONSHIPS/CLASSIFICATIONS]
Domain: [INDUSTRY OR SUBJECT AREA]
Output Format: [STRUCTURED DATABASE/DASHBOARD/API/SEARCH INDEX]

Build the text mining pipeline across these six sections:

1. TEXT PREPROCESSING AND NORMALIZATION
Design a robust text preprocessing pipeline tailored to the document type. Implement document parsing for different formats including PDF extraction with layout preservation using PyMuPDF, HTML cleaning with BeautifulSoup, and Office document parsing with python-docx. Build a text normalization pipeline that handles tokenization with spaCy for linguistic accuracy, sentence boundary detection, lemmatization preserving the base form while respecting part of speech, and stopword removal using domain-customized stopword lists. Address domain-specific preprocessing challenges such as handling abbreviations, acronyms, technical jargon, and code snippets embedded in text. Implement language detection for multilingual corpora and route documents to language-specific processing pipelines.

2. NAMED ENTITY RECOGNITION AND EXTRACTION
Build an entity extraction system that identifies and categorizes key information in the text. Start with spaCy's pre-trained NER models for standard entity types like persons, organizations, locations, and dates. Extend with custom entity types specific to the domain using spaCy's entity ruler for pattern-based extraction and fine-tuned transformer models for complex entities. Implement relation extraction to discover connections between entities such as person works at organization or product has feature. Build an entity resolution layer that links different mentions of the same entity including abbreviations, aliases, and misspellings. Create a structured knowledge graph from extracted entities and relationships using networkx for analysis and visualization.

3. TOPIC MODELING AND DOCUMENT CLUSTERING
Discover the thematic structure of the corpus using topic modeling. Implement Latent Dirichlet Allocation as a baseline using gensim with proper hyperparameter tuning for the number of topics using coherence scores. Build a BERTopic model that leverages transformer embeddings for semantically richer topic representations. Compare both approaches on interpretability, coherence, and alignment with known domain themes. Create dynamic topic modeling to track how topics evolve over time if the corpus has temporal metadata. Implement hierarchical topic structures that allow drilling from broad themes to specific subtopics. Visualize topic distributions using pyLDAvis for LDA and BERTopic's built-in visualization for interactive topic exploration.

4. KEY PHRASE EXTRACTION AND SUMMARIZATION
Extract the most important phrases and generate summaries at multiple levels. Implement extractive key phrase extraction using RAKE, YAKE, and KeyBERT, comparing results and allowing domain-specific tuning. Build extractive summarization that selects the most important sentences using TextRank or a BERT-based approach. Implement abstractive summarization using a fine-tuned T5 or BART model for generating human-readable summaries of long documents. Create multi-document summarization for synthesizing information across related documents. Design a structured information extraction template that pulls specific fields from documents based on predefined schemas, useful for processing contracts, reports, or forms at scale.

5. TEXT CLASSIFICATION AND ROUTING
Build a classification system that automatically categorizes documents. Implement zero-shot classification using large language models for immediate classification without training data. Build supervised classifiers using fine-tuned transformers when labeled training data is available, with active learning to minimize annotation effort. Create a multi-label classification pipeline for documents that belong to multiple categories. Implement confidence calibration so classification scores are meaningful probabilities. Design a human-in-the-loop workflow where low-confidence predictions are routed for manual review, and the corrections feed back into model retraining. Build a document routing system that automatically directs documents to the appropriate team or workflow based on their classified content.

6. PIPELINE INTEGRATION AND SEARCH
Integrate all text mining components into a unified system. Build a document processing pipeline that runs all analysis steps and stores results in a structured database. Implement a semantic search engine using vector embeddings stored in a vector database like FAISS, Pinecone, or Weaviate that allows users to find documents by meaning rather than just keywords. Create a text analytics dashboard using Streamlit or Plotly Dash that provides an overview of the corpus with interactive exploration of topics, entities, and trends. Build an API layer using FastAPI that exposes text analysis capabilities for downstream applications. Design a monitoring system that tracks processing throughput, model accuracy on a validation set, and alerts when document characteristics shift significantly from the training distribution.