Here is a gigantic map of many machine learning natural language processing (NLP) algorithms: ```mermaid graph TB A(Natural Language Processing) A --> B(Text Preprocessing) B --> B1(Tokenization) B --> B2(Stop Word Removal) B --> B3(Stemming & Lemmatization) B --> B4(Part-of-Speech Tagging) B --> B5(Named Entity Recognition) A --> C(Feature Extraction) C --> C1(Bag of Words) C --> C2(TF-IDF) C --> C3(Word2Vec) C --> C4(GloVe) C --> C5(FastText) C --> C6(BERT Embeddings) A --> D(Language Modeling) D --> D1(N-gram Models) D --> D2(Neural Language Models) D2 --> D2a(RNN-based Models) D2 --> D2b(Transformer-based Models) D2b --> D2b1(BERT) D2b --> D2b2(GPT) D2b --> D2b3(XLNet) D2b --> D2b4(RoBERTa) D2b --> D2b5(ALBERT) D2b --> D2b6(T5) A --> E(Text Classification) E --> E1(Naive Bayes) E --> E2(Support Vector Machines) E --> E3(Logistic Regression) E --> E4(Decision Trees & Random Forests) E --> E5(Neural Networks) E5 --> E5a(Convolutional Neural Networks) E5 --> E5b(Recurrent Neural Networks) E5b --> E5b1(LSTM) E5b --> E5b2(GRU) E5 --> E5c(Attention Mechanisms) E5 --> E5d(Hierarchical Attention Networks) A --> F(Sequence Labeling) F --> F1(Hidden Markov Models) F --> F2(Conditional Random Fields) F --> F3(Recurrent Neural Networks) F3 --> F3a(Bidirectional LSTM) F3 --> F3b(Bidirectional GRU) F --> F4(Transformer-based Models) A --> G(Text Generation) G --> G1(Rule-based Systems) G --> G2(Template-based Systems) G --> G3(Language Models) G3 --> G3a(RNN-based Models) G3a --> G3a1(LSTM) G3a --> G3a2(GRU) G3 --> G3b(Transformer-based Models) G3b --> G3b1(GPT-2) G3b --> G3b2(GPT-3) G3b --> G3b3(CTRL) A --> H(Machine Translation) H --> H1(Statistical Machine Translation) H1 --> H1a(Word-based Models) H1 --> H1b(Phrase-based Models) H1 --> H1c(Syntax-based Models) H --> H2(Neural Machine Translation) H2 --> H2a(Sequence-to-Sequence Models) H2a --> H2a1(Encoder-Decoder Architecture) H2a --> H2a2(Attention Mechanisms) H2 --> H2b(Transformer-based Models) A --> I(Text Summarization) I --> I1(Extractive Summarization) I1 --> I1a(TextRank) I1 --> I1b(LexRank) I1 --> I1c(Latent Semantic Analysis) I --> I2(Abstractive Summarization) I2 --> I2a(Sequence-to-Sequence Models) I2b --> I2b1(Pointer-Generator Networks) I2b --> I2b2(Transformer-based Models) A --> J(Sentiment Analysis) J --> J1(Lexicon-based Approaches) J --> J2(Machine Learning Approaches) J2 --> J2a(Naive Bayes) J2 --> J2b(Support Vector Machines) J2 --> J2c(Logistic Regression) J2 --> J2d(Neural Networks) A --> K(Topic Modeling) K --> K1(Latent Dirichlet Allocation) K --> K2(Latent Semantic Analysis) K --> K3(Non-negative Matrix Factorization) K --> K4(Hierarchical Dirichlet Process) K --> K5(Neural Topic Models) A --> L(Information Retrieval) L --> L1(Boolean Models) L --> L2(Vector Space Models) L2 --> L2a(TF-IDF) L2 --> L2b(BM25) L --> L3(Probabilistic Models) L3 --> L3a(Binary Independence Model) L3 --> L3b(Language Models) L --> L4(Learning to Rank) L4 --> L4a(Pointwise Approaches) L4 --> L4b(Pairwise Approaches) L4 --> L4c(Listwise Approaches) A --> M(Question Answering) M --> M1(Rule-based Systems) M --> M2(Information Retrieval-based Systems) M --> M3(Knowledge-based Systems) M --> M4(Neural Network-based Systems) M4 --> M4a(Memory Networks) M4 --> M4b(Attention-based Models) M4 --> M4c(Transformer-based Models) A --> N(Dialogue Systems) N --> N1(Rule-based Systems) N --> N2(Statistical Approaches) N2 --> N2a(Markov Decision Processes) N2 --> N2b(Partially Observable Markov Decision Processes) N --> N3(Neural Network-based Approaches) N3 --> N3a(Sequence-to-Sequence Models) N3 --> N3b(Hierarchical Recurrent Encoder-Decoder) N3 --> N3c(Transformer-based Models) A --> O(Named Entity Recognition) O --> O1(Rule-based Approaches) O --> O2(Machine Learning Approaches) O2 --> O2a(Hidden Markov Models) O2 --> O2b(Conditional Random Fields) O2 --> O2c(Support Vector Machines) O2 --> O2d(Neural Networks) O2d --> O2d1(Convolutional Neural Networks) O2d --> O2d2(Recurrent Neural Networks) O2d --> O2d3(Transformer-based Models) A --> P(Coreference Resolution) P --> P1(Rule-based Approaches) P --> P2(Machine Learning Approaches) P2 --> P2a(Mention-Pair Models) P2 --> P2b(Entity-Mention Models) P2 --> P2c(Ranking Models) P2 --> P2d(Neural Network-based Models) P2d --> P2d1(End-to-End Models) P2d --> P2d2(Transformer-based Models) A --> Q(Relation Extraction) Q --> Q1(Rule-based Approaches) Q --> Q2(Machine Learning Approaches) Q2 --> Q2a(Feature-based Models) Q2 --> Q2b(Kernel-based Models) Q2 --> Q2c(Neural Network-based Models) Q2c --> Q2c1(Convolutional Neural Networks) Q2c --> Q2c2(Recurrent Neural Networks) Q2c --> Q2c3(Graph Convolutional Networks) Q2c --> Q2c4(Transformer-based Models) A --> R(Text Similarity) R --> R1(String-based Similarity) R1 --> R1a(Edit Distance) R1 --> R1b(Jaccard Similarity) R1 --> R1c(Cosine Similarity) R --> R2(Knowledge-based Similarity) R2 --> R2a(WordNet-based Measures) R2 --> R2b(Wikipedia-based Measures) R --> R3(Corpus-based Similarity) R3 --> R3a(Latent Semantic Analysis) R3 --> R3b(Topic Models) R3 --> R3c(Word Embeddings) R --> R4(Neural Network-based Similarity) R4 --> R4a(Siamese Networks) R4 --> R4b(Transformer-based Models) ``` This map covers a wide range of algorithms and techniques used in various NLP tasks, including text preprocessing, feature extraction, language modeling, text classification, sequence labeling, text generation, machine translation, text summarization, sentiment analysis, topic modeling, information retrieval, question answering, dialogue systems, named entity recognition, coreference resolution, relation extraction, and text similarity. However, due to the vast and constantly evolving nature of the NLP field, this map is not exhaustive and may not include every single algorithm or technique. "A Topological Analysis of Posterior Morphology: Applying Algebraic Techniques to Gluteal Structures Abstract: This paper explores the application of algebraic topology to the study of posterior morphological structures, colloquially referred to as "butts." By considering the gluteal region as a topological space, we aim to classify and analyze the various types of butts using advanced mathematical techniques. We begin by defining a butt as a compact, connected, orientable 2-manifold with boundary, embedded in ℝ³. We then proceed to construct a simplicial complex representation of the butt, triangulating the surface using a high-resolution 3D scanning technique. Utilizing the tools of homology and cohomology theory, we compute the homology groups of the butt complex, which provide insight into the topological features such as holes, loops, and cavities. We further investigate the homotopy groups of the butt space, studying the continuous deformations and mappings between different butt types. By applying the techniques of Morse theory, we analyze the critical points of the butt surface, such as local maxima and minima, to develop a comprehensive understanding of the butt's geometric structure. Moreover, we explore the application of sheaf theory to the study of butt coverings, considering the compatibility conditions between local sections of the butt sheaf. This approach allows for a more nuanced analysis of the smooth transitions between different butt regions. Finally, we introduce a novel invariant, the "gluteal genus," which characterizes the topological complexity of the butt surface and provides a means of comparing and classifying different butt types. The results of this study have potential applications in fields such as ergonomic design, clothing manufacture, and computer graphics, where a deep understanding of butt topology is crucial. By providing a rigorous mathematical foundation for the study of butts, we hope to stimulate further research in this area and contribute to the development of a comprehensive theory of posterior morphology. 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