[{"data":1,"prerenderedAt":1294},["ShallowReactive",2],{"subject:natural-language-processing":3,"course-wordcounts":63,"nav:natural-language-processing":975},{"id":4,"title":5,"blurb":6,"body":7,"brief":14,"category":46,"description":47,"draft":48,"extension":49,"meta":50,"module":11,"navigation":21,"path":51,"practice":52,"rawbody":53,"readingTime":54,"seo":57,"sources":58,"status":59,"stem":60,"summary":11,"topics":61,"__hash__":62},"course\u002F09.natural-language-processing\u002Findex.md","Natural Language Processing","How machines read, represent, and generate human language — from regular\nexpressions and n-grams to embeddings, transformers, and LLMs.\n",{"type":8,"value":9,"toc":10},"minimark",[],{"title":11,"searchDepth":12,"depth":12,"links":13},"",2,[],[15,17,22,24,28,30,34,36,40,42],{"p":16},"Every sentence of human language is ambiguous, context-dependent, and\nfull of knowledge the words never state — and we want machines to read\nit, translate it, answer from it, and write it back.\n",{"fig":18,"n":19,"caption":20,"large":21},"attention","001","Self-attention: each word draws on every other, weighted by relevance.\n",true,{"p":23},"The field begins with the basics: \u003Cstrong>regular expressions\u003C\u002Fstrong> to\nmatch patterns, \u003Cstrong>tokenization\u003C\u002Fstrong> to split text into units, and\n\u003Cstrong>edit distance\u003C\u002Fstrong> to measure how far apart two strings are.\nThen the first real model of language — the \u003Cstrong>n-gram\u003C\u002Fstrong>, which\npredicts the next word from the last few.\n",{"fig":25,"n":26,"caption":27},"ngram","002","An n-gram model: a window slides across the text, three tokens at a time.\n",{"p":29},"The modern turn is \u003Cem>representation\u003C\u002Fem>: instead of treating words as\natomic symbols, embed them as vectors so that meaning becomes geometry —\nking minus man plus woman lands near queen — and let a neural network\ncompose those vectors into the meaning of a whole sentence.\n",{"fig":31,"n":32,"caption":33},"embed","003","Word embeddings: meaning as geometry — king − man + woman ≈ queen.\n",{"p":35},"The \u003Cstrong>transformer\u003C\u002Fstrong> lets every word attend to every other,\nand scaled up on enough text it became the large language model — one\narchitecture that translates, summarizes, answers, and converses.\n",{"fig":37,"n":38,"caption":39},"parsetree","004","A parse tree: words group into phrases and phrases into a sentence.\n",{"p":41},"From ELIZA's pattern-matching in 1966 to models that pass the bar exam,\nthe problem is the same: turn the fluid, ambiguous signal of language\ninto something a computer can compute with.\n",{"fig":43,"n":44,"caption":45},"seqtag","005","Sequence tagging: a part-of-speech tag drops in under each word.\n","computer-science","The computational treatment of human language: regular expressions, tokenization,\nand edit distance; n-gram language models; text classification with naive Bayes\nand logistic regression; vector semantics and word embeddings; neural language\nmodels, RNNs, and LSTMs; the transformer and large language models; fine-tuning\nand prompting; machine translation, question answering, and dialogue; and speech\nrecognition. Built on Jurafsky & Martin's _Speech and Language Processing_. Notes\nfor this subject are coming soon.\n",false,"md",{},"\u002Fnatural-language-processing",[],"---\ntitle: Natural Language Processing\nstatus: available\nblurb: |\n  How machines read, represent, and generate human language — from regular\n  expressions and n-grams to embeddings, transformers, and LLMs.\ndescription: |\n  The computational treatment of human language: regular expressions, tokenization,\n  and edit distance; n-gram language models; text classification with naive Bayes\n  and logistic regression; vector semantics and word embeddings; neural language\n  models, RNNs, and LSTMs; the transformer and large language models; fine-tuning\n  and prompting; machine translation, question answering, and dialogue; and speech\n  recognition. Built on Jurafsky & Martin's _Speech and Language Processing_. Notes\n  for this subject are coming soon.\nbrief:\n  - p: |\n      Every sentence of human language is ambiguous, context-dependent, and\n      full of knowledge the words never state — and we want machines to read\n      it, translate it, answer from it, and write it back.\n  - fig: attention\n    n: \"001\"\n    caption: |\n      Self-attention: each word draws on every other, weighted by relevance.\n    large: true\n  - p: |\n      The field begins with the basics: \u003Cstrong>regular expressions\u003C\u002Fstrong> to\n      match patterns, \u003Cstrong>tokenization\u003C\u002Fstrong> to split text into units, and\n      \u003Cstrong>edit distance\u003C\u002Fstrong> to measure how far apart two strings are.\n      Then the first real model of language — the \u003Cstrong>n-gram\u003C\u002Fstrong>, which\n      predicts the next word from the last few.\n  - fig: ngram\n    n: \"002\"\n    caption: |\n      An n-gram model: a window slides across the text, three tokens at a time.\n  - p: |\n      The modern turn is \u003Cem>representation\u003C\u002Fem>: instead of treating words as\n      atomic symbols, embed them as vectors so that meaning becomes geometry —\n      king minus man plus woman lands near queen — and let a neural network\n      compose those vectors into the meaning of a whole sentence.\n  - fig: embed\n    n: \"003\"\n    caption: |\n      Word embeddings: meaning as geometry — king − man + woman ≈ queen.\n  - p: |\n      The \u003Cstrong>transformer\u003C\u002Fstrong> lets every word attend to every other,\n      and scaled up on enough text it became the large language model — one\n      architecture that translates, summarizes, answers, and converses.\n  - fig: parsetree\n    n: \"004\"\n    caption: |\n      A parse tree: words group into phrases and phrases into a sentence.\n  - p: |\n      From ELIZA's pattern-matching in 1966 to models that pass the bar exam,\n      the problem is the same: turn the fluid, ambiguous signal of language\n      into something a computer can compute with.\n  - fig: seqtag\n    n: \"005\"\n    caption: |\n      Sequence tagging: a part-of-speech tag drops in under each word.\n---\n",{"text":55,"minutes":56,"time":56,"words":56},"0 min 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Is Natural Language Processing?","\u002Fnatural-language-processing\u002Ffoundations\u002Fwhat-is-nlp",[977],"Natural language processing is the computational treatment of human language: reading it, representing it, and generating it. We set up why the problem is hard — ambiguity at every level, from sound to intent — trace the field from ELIZA's pattern-matching through statistical methods to today's neural models, lay out the linguistic levels and task families the course covers, and fix the vocabulary of tokens, types, and corpora the rest of the notes rely on.\n",{"title":987,"path":988,"lessonNumber":12,"topics":989,"summary":990},"Regular Expressions and Text Normalization","\u002Fnatural-language-processing\u002Ffoundations\u002Fregex-and-text-normalization",[977],"Before any model touches text, the text has to be found and cleaned. Regular expressions give an algebra for describing string patterns; tokenization, case folding, and stemming turn raw characters into the units a model counts; and byte-pair encoding builds a subword vocabulary that spells out any word. Measuring how far apart two strings are — minimum edit distance — is the next lesson.\n",{"title":992,"path":993,"lessonNumber":994,"topics":995,"summary":996},"Minimum Edit Distance","\u002Fnatural-language-processing\u002Ffoundations\u002Fminimum-edit-distance",3,[977],"Much of language processing needs to measure how similar two strings are — a speller ranking corrections, a diff tool, a coreference resolver. Minimum edit distance counts the insertions, deletions, and substitutions that turn one string into another, computed by a dynamic-programming table. We fill the table for intention to execution, backtrace to recover the alignment, and see how the same machinery generalizes to weighted edits, Viterbi, and biological sequence alignment.\n",{"title":998,"path":999,"lessonNumber":1000,"topics":1001,"summary":1002},"N-Gram Language Models","\u002Fnatural-language-processing\u002Ffoundations\u002Fn-gram-language-models",4,[977],"A language model assigns a probability to a sequence of words and, equivalently, predicts the next word from its history. The n-gram model makes this tractable by truncating the history to the last few words, estimates the resulting conditional probabilities by counting, and is scored by perplexity. We build the model from the chain rule, work a bigram example on a small corpus, and read perplexity as a branching factor. The next lesson covers the zero counts that break this model and the smoothing that repairs them.\n",{"title":1004,"path":1005,"lessonNumber":1006,"topics":1007,"summary":1008},"Smoothing and Backoff","\u002Fnatural-language-processing\u002Ffoundations\u002Fsmoothing-and-backoff",5,[977],"Every finite corpus is missing good word sequences it simply never saw, so a raw n-gram model assigns them probability zero and breaks. Smoothing repairs the zeros: add-one and add-k shave mass off seen events, backoff and interpolation fall back on shorter contexts, and Kneser-Ney — worked here by hand — replaces raw frequency with how many contexts a word completes. We close on web-scale stupid backoff and the neural models that dissolve the zero problem rather than patch it.\n",{"module":1010,"moduleNumber":12,"slug":1011,"lessons":1012},"Text Classification","classification",[1013,1019,1024,1029],{"title":1014,"path":1015,"lessonNumber":978,"topics":1016,"summary":1018},"Naive Bayes and Sentiment Classification","\u002Fnatural-language-processing\u002Fclassification\u002Fnaive-bayes-and-sentiment",[1017],"Classification","Text classification assigns a category to a document — positive or negative, spam or not, one topic among many. Naive Bayes is a generative solution: apply Bayes' rule, assume the words are conditionally independent given the class, and the winning class is the one maximizing the product of a prior and per-word likelihoods. We train it by counting with add-one smoothing, work a full sentiment example by hand, sharpen it for sentiment (binary counts, negation, lexicons), and place it among the transformer classifiers that came after.\n",{"title":1020,"path":1021,"lessonNumber":12,"topics":1022,"summary":1023},"Evaluating Classifiers","\u002Fnatural-language-processing\u002Fclassification\u002Fevaluating-classifiers",[1017],"A trained classifier is only useful once we can measure how good it is. We build the confusion matrix, see why accuracy misleads on unbalanced data, and define precision, recall, and the F-measure that balances them. Multi-class tasks need macro- versus micro-averaging; reliable estimates need cross-validation. We close on statistical significance — the paired bootstrap test for whether one system's lead over another is significant.\n",{"title":1025,"path":1026,"lessonNumber":994,"topics":1027,"summary":1028},"Logistic Regression","\u002Fnatural-language-processing\u002Fclassification\u002Flogistic-regression",[1017],"Logistic regression is the discriminative counterpart to naive Bayes: instead of modelling how a document is generated, it learns weights that directly separate the classes. We build it from the sigmoid, derive the cross-entropy loss from maximum likelihood, learn the weights by stochastic gradient descent, regularize to curb overfitting, and generalize to many classes with the softmax. The two-class model is already a one-neuron network, so this is the bridge to neural language models.\n",{"title":1030,"path":1031,"lessonNumber":1000,"topics":1032,"summary":1033},"Sentiment and Affect Lexicons","\u002Fnatural-language-processing\u002Fclassification\u002Fsentiment-and-affect-lexicons",[1017],"A sentiment lexicon is a list of words annotated with the affective meaning they carry — positive or negative, or scores along valence, arousal, and dominance. We fix what \"emotion\" means (basic-emotion versus dimensional models), survey the standard lexicons, and then build lexicons three ways: by human labeling with best-worst scaling, by semi-supervised induction from seed words over an embedding space, and by supervised learning from starred reviews. We close on connotation frames, which record the sentiment a verb implies about each of its arguments.\n",{"module":1035,"moduleNumber":994,"slug":1036,"lessons":1037},"Semantics","semantics",[1038,1043,1048],{"title":1039,"path":1040,"lessonNumber":978,"topics":1041,"summary":1042},"Vector Semantics and Embeddings","\u002Fnatural-language-processing\u002Fsemantics\u002Fvector-semantics-and-embeddings",[1035],"Vector semantics represents a word's meaning as a point in space, derived from the company the word keeps. This first part builds the count-based side: the distributional hypothesis, co-occurrence matrices in their term-document and word-word forms, cosine as the similarity measure, and the two weightings — tf-idf and PPMI — that fix what raw counts get wrong. The result is a sparse, interpretable vector for every word, and the setup for the dense embeddings of the next lesson.\n",{"title":1044,"path":1045,"lessonNumber":12,"topics":1046,"summary":1047},"Static Word Embeddings: word2vec and After","\u002Fnatural-language-processing\u002Fsemantics\u002Fstatic-word-embeddings",[1035],"Count-based vectors are long and sparse; embeddings are the short, dense alternative. This lesson builds them with word2vec's skip-gram and negative sampling — a classifier whose learned weights are the vectors — derives its gradient, and works one update by hand. It then reads relations off the analogy parallelogram, surveys the papers that framed the static-embedding era (word2vec, GloVe, the SGNS-as-PPMI equivalence, fastText, ELMo), and closes on the biases embeddings inherit and the single-vector-per-word ceiling that contextual models break.\n",{"title":1049,"path":1050,"lessonNumber":994,"topics":1051,"summary":1052},"Neural Networks and Neural Language Models","\u002Fnatural-language-processing\u002Fsemantics\u002Fneural-language-models",[1035],"A neural network is a stack of units, each a weighted sum passed through a non-linearity — a single unit on its own is logistic regression. We build the network up from that unit: the activation functions that give it power, the XOR problem that forces a hidden layer, the feedforward forward pass in matrix form, and the Bengio-style feedforward neural language model that concatenates word embeddings and predicts the next word with a softmax. Training is cross-entropy minimized by gradient descent, with backpropagation supplying the gradient. Embeddings let the model share statistical strength across similar words, avoiding the sparsity that limits n-gram models.\n",{"module":1054,"moduleNumber":1000,"slug":1055,"lessons":1056},"Sequences","sequences",[1057,1062,1067],{"title":1058,"path":1059,"lessonNumber":978,"topics":1060,"summary":1061},"Sequence Labeling: POS and NER","\u002Fnatural-language-processing\u002Fsequences\u002Fsequence-labeling",[1054],"Sequence labeling assigns one tag to every token in a sentence. This first part sets up the task through its two canonical cases — part-of-speech tagging over the Penn Treebank tagset, and named-entity recognition reframed as token labeling with the BIO scheme — then builds the hidden Markov model, the classic probabilistic tagger. The HMM tags by Bayesian inference: transition and emission probabilities under two Markov assumptions, reducing tagging to an argmax over tag sequences. That argmax is exponential to enumerate, which sets up the Viterbi decoder, the CRF, and neural taggers of the next lesson.\n",{"title":1063,"path":1064,"lessonNumber":12,"topics":1065,"summary":1066},"Viterbi Decoding, CRFs, and Neural Taggers","\u002Fnatural-language-processing\u002Fsequences\u002Fcrfs-and-neural-taggers",[1054],"The HMM reduced tagging to an argmax over exponentially many tag sequences. This lesson builds the decoder that makes it tractable — the Viterbi dynamic program, worked through a full numeric trace on real WSJ probabilities — then keeps that same decoder while replacing the HMM's rigid tables. The linear-chain conditional random field is a discriminative log-linear model whose global feature functions can inspect any part of the input, which is why CRFs win for NER. Finally it traces the shift to neural taggers (biLSTM-CRF, character-aware NER, ELMo), where hand-built features become learned representations while the Viterbi decoder carries over unchanged.\n",{"title":1068,"path":1069,"lessonNumber":994,"topics":1070,"summary":1071},"RNNs and LSTMs","\u002Fnatural-language-processing\u002Fsequences\u002Frnns-and-lstms",[1054],"A feedforward neural language model sees a fixed window of words and can look no further back. The recurrent neural network removes that limit: it carries a hidden state across time, so each word is read in the context of everything before it. We build the RNN from its one recurrent equation, use it as a language model, train it by backpropagation through time, and diagnose the vanishing-gradient problem that makes plain RNNs forget. The LSTM fixes the forgetting with a cell state and three gates, and the encoder-decoder stacks two RNNs into a sequence-to-sequence model — and its single-vector bottleneck is the problem attention was invented to remove.\n",{"module":1073,"moduleNumber":1006,"slug":1074,"lessons":1075},"Transformers","transformers",[1076,1081,1086,1091,1096,1101],{"title":1077,"path":1078,"lessonNumber":978,"topics":1079,"summary":1080},"Transformers and Self-Attention","\u002Fnatural-language-processing\u002Ftransformers\u002Ftransformers-and-attention",[1073],"Recurrence forced language models to read one word at a time and to squeeze every dependency through a chain of hidden states. Self-attention removes the recurrence: at every layer each position compares itself to every other and reads a weighted mixture of them, in a single parallel step. This first part builds the attention operation from the ground up — the soft lookup, queries and keys and values, the scaled dot-product, the numeric trace, the matrix form, and the causal mask — and sets up the full transformer architecture that follows.\n",{"title":1082,"path":1083,"lessonNumber":12,"topics":1084,"summary":1085},"The Transformer Architecture","\u002Fnatural-language-processing\u002Ftransformers\u002Fthe-transformer-architecture",[1073],"This part takes the scaled dot-product attention of the previous lesson and assembles the full transformer architecture around it: multi-head attention so several relations can be read at once, the transformer block of residual connections and layer norm that makes deep stacks trainable, positional embeddings that restore word order, the decoder-only language model, and the encoder, decoder, and encoder-decoder shapes — closing with the 2017 paper and the pre-norm, FlashAttention, and RoPE refinements that scaled it up.\n",{"title":1087,"path":1088,"lessonNumber":994,"topics":1089,"summary":1090},"Large Language Models","\u002Fnatural-language-processing\u002Ftransformers\u002Flarge-language-models",[1073],"A large language model is a decoder-only transformer trained on one objective — predict the next token. This first part assembles the inference side: the language-modeling head that turns a hidden state into a distribution over the vocabulary, autoregressive generation, and the decoding strategies — greedy, beam, and sampling with temperature, top-k, and nucleus — that read text back out of that distribution. Training the distribution at web scale comes next.\n",{"title":1092,"path":1093,"lessonNumber":1000,"topics":1094,"summary":1095},"Large Language Models: Pretraining and Scaling","\u002Fnatural-language-processing\u002Ftransformers\u002Fllm-pretraining-and-scaling",[1073],"A language model's next-token distribution is only as good as the parameters behind it. This part is where those parameters come from: self-supervised pretraining on web-scale text with teacher forcing and cross-entropy, the scaling laws that make test loss a predictable power law in parameters, data, and compute, the KV cache that keeps long-context inference affordable, and how a finished model is evaluated by perplexity and benchmarks — closing with the Kaplan, Chinchilla, GPT-3, and emergence papers behind the scaling story.\n",{"title":1097,"path":1098,"lessonNumber":1006,"topics":1099,"summary":1100},"Fine-Tuning and Prompting","\u002Fnatural-language-processing\u002Ftransformers\u002Ffine-tuning-and-prompting",[1073],"A pretrained transformer is a general-purpose knowledge source; a task is what you do with it. There are two ways to adapt one, and this first part covers the one that updates the weights: fine-tuning. A bidirectional encoder like BERT is pretrained by masked language modeling, then a small task head is bolted on and the whole thing is trained on labelled data for classification, sequence labeling, or span-based question answering — with parameter-efficient variants (adapters, LoRA) that touch only a sliver of the weights. Prompting, the family that leaves the weights frozen, comes next.\n",{"title":1102,"path":1103,"lessonNumber":1104,"topics":1105,"summary":1106},"Prompting and Alignment","\u002Fnatural-language-processing\u002Ftransformers\u002Fprompting-and-alignment",6,[1073],"Fine-tuning adapts a model by changing its weights. The second family of adaptation changes nothing: a large frozen model performs a task from an instruction and a few examples placed in its context. This part covers prompting and in-context learning, chain-of-thought that elicits reasoning, and the two training stages — instruction tuning and RLHF — that turn a fluent base predictor into an aligned assistant, closing with the BERT, LoRA, chain-of-thought, InstructGPT, and retrieval-augmentation papers behind the modern adaptation pipeline.\n",{"module":1108,"moduleNumber":1104,"slug":1109,"lessons":1110},"Linguistic Structure","linguistic-structure",[1111,1117,1122,1127,1132,1137,1142,1148,1154,1160,1166,1172,1178,1184,1190,1196,1202,1208,1214,1220],{"title":1112,"path":1113,"lessonNumber":978,"topics":1114,"summary":1116},"Constituency Parsing","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-parsing",[1115],"Structure","A constituency parse groups a sentence into nested phrases described by a context-free grammar. We build the CFG formalism, read the phrase structure of English off a treebank, confront the structural ambiguity that makes parsing hard, convert to Chomsky normal form, and then solve it with CKY — the dynamic-programming chart that fills a triangular table bottom-up. Probabilistic and neural span parsers, evaluation, and shallow parsing follow in the companion lesson.\n",{"title":1118,"path":1119,"lessonNumber":12,"topics":1120,"summary":1121},"CKY Scoring, Evaluation, and Shallow Parsing","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcky-scoring-and-evaluation",[1115],"The CKY chart returns every parse but does not say which is correct. Disambiguation needs a score on trees. This lesson attaches probabilities to a grammar (the PCFG and lexicalization), replaces the grammar with a neural span scorer over a pretrained encoder, states the self-attentive results that made it the state of the art, evaluates parsers against a treebank with PARSEVAL, and closes with chunking and shallow parsing for tasks that need only the flat phrases.\n",{"title":1123,"path":1124,"lessonNumber":994,"topics":1125,"summary":1126},"Dependency Parsing","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdependency-parsing",[1115],"A dependency parse throws away phrases and keeps only directed, labeled arcs from heads to their dependents, so the subject and object of a verb hang off the verb directly. We fix the formalism (rooted trees, typed Universal-Dependency relations, projectivity), then build the first parser family: transition-based arc-standard and arc-eager parsing, a greedy stack-and-buffer machine trained from an oracle. Graph-based and neural dependency parsing follow in the companion lesson.\n",{"title":1128,"path":1129,"lessonNumber":1000,"topics":1130,"summary":1131},"Graph-Based and Neural Dependency Parsing","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fgraph-based-and-neural-dependency-parsing",[1115],"Greedy transition parsing commits locally; the graph-based family scores whole trees instead. This lesson scores every candidate head-dependent edge and extracts the maximum spanning tree with Chu-Liu\u002FEdmonds, develops the biaffine neural scorer that made graph-based parsing the accuracy leader, evaluates parsers with the unlabeled and labeled attachment scores (UAS and LAS), and closes on where the two parser families sit and what they feed downstream.\n",{"title":1133,"path":1134,"lessonNumber":1006,"topics":1135,"summary":1136},"Word Senses and Disambiguation","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fword-senses-and-wsd",[1115],"A word is not an atom of meaning: \"bass\" names a fish, a voice, and an instrument, and one static embedding blurs them into a single point. This lesson pulls those senses apart. We define polysemy and the relations that organize senses — synonymy, antonymy, hyponymy, meronymy — build them into WordNet's synset graph, measure similarity along that graph, and then solve the core of word sense disambiguation: the most-frequent-sense baseline, the Lesk gloss-overlap algorithm, feature-based classifiers, and the nearest-neighbor method over BERT embeddings. WSD variants, embeddings, and evaluation follow in the companion lesson.\n",{"title":1138,"path":1139,"lessonNumber":1104,"topics":1140,"summary":1141},"WSD in Practice and Word Sense Induction","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fwsd-in-practice-and-induction",[1115],"Beyond core word sense disambiguation lie the variants and loose ends: the sense-inventory-free Word-in-Context task, retrofitting static embeddings to a thesaurus, discovering senses without a fixed inventory (word sense induction), the gloss-aware and bi-encoder neural systems that hold the state of the art, and how WSD and its cousins are evaluated. Together they connect one-vector-per-word embeddings to sense-aware contextual representations.\n",{"title":1143,"path":1144,"lessonNumber":1145,"topics":1146,"summary":1147},"Semantic Roles and Information Extraction","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-roles-and-information-extraction",7,[1115],"Semantic roles answer \"who did what to whom\" for a single event, abstracting away the syntax that expresses it. We show why syntax alone is not enough, generalize over diathesis alternations with thematic roles, number a predicate's arguments with PropBank and group predicates into frames with FrameNet, tag each argument automatically with semantic role labeling, and factor predicates into primitives. Information extraction scales the idea to a corpus in the companion lesson.\n",{"title":1149,"path":1150,"lessonNumber":1151,"topics":1152,"summary":1153},"Relations, Events, and Templates","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Frelations-events-and-templates",8,[1115],"Semantic roles answer \"who did what\" for one predicate; information extraction scales the idea to a whole corpus. This lesson turns unstructured text into structured data: relation extraction pulls entity-relation-entity triples out of sentences by patterns, supervision, and distant supervision; event and temporal extraction place those facts on a timeline; and template filling and knowledge-base population assemble them into a database a downstream system can query.\n",{"title":1155,"path":1156,"lessonNumber":1157,"topics":1158,"summary":1159},"Coreference and Discourse","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoreference-and-discourse",9,[1115],"A text is more than a bag of sentences: entities recur under different names. Coreference resolution links every mention to the discourse entity it evokes — the linguistic background of pronouns, definite NPs, and names; mention detection; the mention-pair, mention-ranking, and entity-based architectures; a neural end-to-end span model that scores candidate antecedents; features, evaluation by the CoNLL F1, gender bias, and the neural coreference lineage. Discourse coherence follows in the companion lesson.\n",{"title":1161,"path":1162,"lessonNumber":1163,"topics":1164,"summary":1165},"Coherence and Discourse Structure","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcoherence-and-discourse-structure",10,[1115],"Coherence is what makes a run of sentences a discourse rather than an arbitrary collection. This lesson develops coherence relations and Rhetorical Structure Theory trees, discourse-structure parsing, Centering and the entity grid for entity-based coherence, and representation-learning models of local coherence, measured in part over the coreference chains recovered in the companion lesson.\n",{"title":1167,"path":1168,"lessonNumber":1169,"topics":1170,"summary":1171},"Logical Representations of Meaning","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Flogical-semantics",11,[1115],"A meaning representation turns a sentence into a formal structure a machine can check against a world and reason over. We set the desiderata a good representation must meet, ground truth in a model, build up first-order logic for sentences with its connectives, quantifiers, and inference, and reify events with the neo-Davidsonian event variable to escape fixed predicate arity. The compositional lambda calculus, quantifier scope, and description logics follow in the companion lesson.\n",{"title":1173,"path":1174,"lessonNumber":1175,"topics":1176,"summary":1177},"Compositional Semantics and Description Logics","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fcompositional-semantics-and-description-logics",12,[1115],"How do you compute a logical form from a sentence automatically? This lesson builds the compositional machinery: the lambda calculus that assembles a formula from a parse tree one beta-reduction at a time, the quantifier-scope ambiguity a single syntax tree leaves open, and the decidable description logics — TBox, ABox, subsumption, role restrictions — behind the Web Ontology Language, closing with how the map from string to logical form can be learned.\n",{"title":1179,"path":1180,"lessonNumber":1181,"topics":1182,"summary":1183},"Semantic Parsing","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fsemantic-parsing",13,[1115],"Turning a sentence into a structured, executable meaning, the grammar-based way. We take the logical forms defined earlier and build them compositionally: a rule-based parser that walks a syntax tree applying lambda terms, then Combinatory Categorial Grammar (CCG), which fuses syntax and semantics so one lexicalized derivation produces both — including supertagging and A* parsing. Learned and neural semantic parsers follow in the companion lesson.\n",{"title":1185,"path":1186,"lessonNumber":1187,"topics":1188,"summary":1189},"Learned and Neural Semantic Parsing","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fneural-semantic-parsing",14,[1115],"Hand-writing a lexicon of lambda terms does not scale, so this lesson learns the parser instead. We cover the two supervision regimes (from logical forms and from denotations), Abstract Meaning Representation as a rooted concept graph, neural sequence-to-sequence parsing with constrained decoding and copy mechanisms, executable text-to-SQL and knowledge-based question answering, the practical systems that made learned parsers accurate, and how the task is evaluated.\n",{"title":1191,"path":1192,"lessonNumber":1193,"topics":1194,"summary":1195},"Information Extraction","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Finformation-extraction",15,[1115],"Information extraction turns free text into a database, and the first step is relation extraction: pulling entity-relation-entity triples out of sentences. We cover all five families — hand-built patterns, supervised classifiers, semi-supervised bootstrapping, distant supervision, and unsupervised Open IE — with worked bootstrapping and distant-supervision traces, then the neural and LLM systems that extended them. Times, events, and templates follow in the companion lesson.\n",{"title":1197,"path":1198,"lessonNumber":1199,"topics":1200,"summary":1201},"Extracting Times, Events, and Templates","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftimes-events-and-templates",16,[1115],"Once relation extraction has produced typed triples, the information-extraction pipeline still has to place facts in time and assemble them into records. This lesson detects and normalizes temporal expressions to ISO 8601 values, detects events and orders them on a timeline with the 13 Allen relations, and fills slot-and-filler templates — flat and hierarchical — for stereotyped situations, closing the loop from text to a queryable database.\n",{"title":1203,"path":1204,"lessonNumber":1205,"topics":1206,"summary":1207},"Discourse Coherence","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fdiscourse-coherence",17,[1115],"A text is more than a set of sentences. What binds a run of sentences into a discourse is coherence, and one of its sources is structured relations between clauses. This lesson develops relational coherence — RST and the PDTB models of coherence relations — and discourse-structure parsing: EDU segmentation and shift-reduce RST parsing, then PDTB relation classification. Entity-based and global coherence follow in the companion lesson.\n",{"title":1209,"path":1210,"lessonNumber":1211,"topics":1212,"summary":1213},"Entity-Based and Global Coherence","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fentity-based-and-global-coherence",18,[1115],"A text coheres not only through relations between clauses but by staying about the same entities and the same topic, and by obeying the macro-structure of its genre. This lesson develops Centering Theory and the entity grid for entity-based coherence, representation-learning models of local coherence, and global coherence — topic segmentation, narrative and argumentation structure, and scientific discourse — then the neural models that learn each.\n",{"title":1215,"path":1216,"lessonNumber":1217,"topics":1218,"summary":1219},"Constituency Grammars","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Fconstituency-grammars",19,[1115],"A constituency grammar is the declarative theory of sentence structure that a parser operates on. We build the context-free grammar formalism from its four parts, show how derivations become parse trees, and work through the phrase structure of English — noun phrases, verb phrases and their subcategorization frames, agreement, coordination, and long-distance dependencies. The treebank, normal-form, and lexicalized views follow in the companion lesson.\n",{"title":1221,"path":1222,"lessonNumber":1223,"topics":1224,"summary":1225},"Treebanks and Lexicalized Grammars","\u002Fnatural-language-processing\u002Flinguistic-structure\u002Ftreebanks-and-lexicalized-grammars",20,[1115],"Where does a grammar come from, and how is it prepared for a parser? We read a context-free grammar off the Penn Treebank, normalize it to Chomsky Normal Form for the CKY chart, then invert the phrase-structure emphasis with lexicalized grammars — Combinatory Categorial Grammar and its slash categories — and close with the grammar's fate in the neural era: span scoring, self-attention, and grammar induction.\n",{"module":1227,"moduleNumber":1145,"slug":1228,"lessons":1229},"Applications","applications",[1230,1235,1240,1245,1250,1255,1260,1265],{"title":1231,"path":1232,"lessonNumber":978,"topics":1233,"summary":1234},"Machine Translation","\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation",[1227],"Machine translation is the task that built the modern toolkit: the encoder-decoder was invented for it, attention was invented to fix its fixed-context bottleneck, and both were later folded into the general transformer. We work through why translation is hard (word order, morphology, lexical and structural divergences), the sequence-to-sequence model and its attention mechanism, transformer-based NMT with cross-attention, subword tokenization with a shared vocabulary, beam-search decoding, and evaluation by BLEU and its successors chrF, BERTScore, and COMET — closing on multilingual and low-resource translation and backtranslation.\n",{"title":1236,"path":1237,"lessonNumber":12,"topics":1238,"summary":1239},"Machine Translation: Decoding, Evaluation, and Scale","\u002Fnatural-language-processing\u002Fapplications\u002Fmachine-translation-decoding-and-evaluation",[1227],"Having built the transformer translation model, we now decode from it and measure the output. Beam search turns the decoder's per-step distributions into a single output string; length normalization keeps it from favoring short translations. We then score translations automatically — BLEU with its n-gram precision, clipping, and brevity penalty, worked through by hand, then its successors chrF, BERTScore, and COMET — and close on the parts of MT that scale beyond one language pair: multilingual and low-resource translation, backtranslation, gender bias, and the lineage from the Transformer to massively multilingual models like NLLB-200.\n",{"title":1241,"path":1242,"lessonNumber":994,"topics":1243,"summary":1244},"Question Answering","\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering",[1227],"A question-answering system takes a natural-language question and returns an answer, not a ranked list of documents. Almost every modern system is built on one pattern: retrieve then read. We start with the information-retrieval machinery that finds candidate text — tf-idf and BM25 term weighting, a worked ranking example, the inverted index, and dense embedding retrieval — then build the retriever-reader pipeline that extracts an answer span with BERT and trace a full retrieve-and-read example end to end.\n",{"title":1246,"path":1247,"lessonNumber":1000,"topics":1248,"summary":1249},"Question Answering: Knowledge Bases and Language Models","\u002Fnatural-language-processing\u002Fapplications\u002Fquestion-answering-knowledge-and-llms",[1227],"The retrieve-and-read pipeline extracts an answer span from prose, but not all knowledge lives in prose. This part covers the rest of the QA stack: entity linking (Wikification) that grounds a question's entities to a knowledge base, knowledge-based QA by semantic parsing a question into an executable query, and the modern default — closed-book QA and retrieval-augmented generation with a large language model — closing on the DPR\u002FRAG\u002Ffusion-in-decoder lineage and how factoid answers are scored by exact match and F1.\n",{"title":1251,"path":1252,"lessonNumber":1006,"topics":1253,"summary":1254},"Dialogue and Chatbots","\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-and-chatbots",[1227],"Conversation is the most natural interface to a machine and one of the hardest to build. We set up what makes human dialogue work — turns, speech acts, grounding, and the local structure of adjacency pairs — then trace the two traditions that answer it: chatbots built to chat (ELIZA's pattern-matching, corpus retrieval, and seq2seq generation with its blandness problem) and task-oriented systems built to get something done (the GUS frame-and-slot architecture and the modern NLU \u002F state-tracker \u002F policy \u002F NLG pipeline that accumulates a frame across turns).\n",{"title":1256,"path":1257,"lessonNumber":1104,"topics":1258,"summary":1259},"Dialogue Systems: LLM Assistants, Evaluation, and Design","\u002Fnatural-language-processing\u002Fapplications\u002Fdialogue-systems-and-assistants",[1227],"Two dialogue traditions — chatbots built to chat and task-oriented frame systems built to get something done — met in the aligned LLM assistant. Instruction tuning plus RLHF fold chit-chat and task dialogue into one model; the LaMDA \u002F InstructGPT \u002F ChatGPT lineage fills in how. The lesson then turns to evaluation (human ratings and acute-eval for chatbots, task success and slot error rate for task systems), user-centered design with Wizard-of-Oz prototyping, and the ethical stakes of building agents people talk to.\n",{"title":1261,"path":1262,"lessonNumber":1145,"topics":1263,"summary":1264},"Text Summarization","\u002Fnatural-language-processing\u002Fapplications\u002Ftext-summarization",[1227],"Summarization compresses a document to its essential meaning, by either selecting sentences to keep (extractive) or writing new ones (abstractive). This part fixes the task and its flavors — single vs. multi-document, generic vs. query-focused, extractive vs. abstractive — then works through extractive summarization in full: scoring by position and centrality, the TextRank\u002FLexRank graph algorithm run as PageRank over a sentence-similarity graph with a worked iteration, and supervised sentence selection.\n",{"title":1266,"path":1267,"lessonNumber":1151,"topics":1268,"summary":1269},"Abstractive Summarization and Evaluation","\u002Fnatural-language-processing\u002Fapplications\u002Fabstractive-summarization-and-evaluation",[1227],"Extractive methods can only reuse the source's own sentences; to compress within a sentence or paraphrase, a summarizer has to generate. This part covers abstractive summarization: the sequence-to-sequence approach, the pointer-generator's copy switch and coverage mechanism, pretrained summarizers (BART, PEGASUS) and zero-shot LLM prompting, the long-document and factuality problems, and ROUGE evaluation with a worked example and its limits — closing on the abstractive lineage from See 2017 through faithfulness metrics.\n",{"module":1271,"moduleNumber":1151,"slug":1272,"lessons":1273},"Speech","speech",[1274,1279,1284,1289],{"title":1275,"path":1276,"lessonNumber":978,"topics":1277,"summary":1278},"Phonetics","\u002Fnatural-language-processing\u002Fspeech\u002Fphonetics",[1271],"Before a recognizer can read speech it has to know what speech is. This first part covers the linguistic substrate: phones and their transcription in the IPA and ARPAbet; articulatory phonetics — how the vocal tract shapes airflow into consonants and vowels; and prosody — stress, tune, and the F0 contour. The acoustic side — the waveform, its spectrum, formants, and the spectrogram — is the second part.\n",{"title":1280,"path":1281,"lessonNumber":12,"topics":1282,"summary":1283},"Acoustic Phonetics","\u002Fnatural-language-processing\u002Fspeech\u002Facoustic-phonetics",[1271],"Articulation is the cause; the acoustic signal is the effect, and the effect is all a microphone ever gets. This part follows the sound out of the mouth: waves, sampling and the Nyquist limit, F0 and the pitch track, the mel scale, the spectrum and Fourier analysis, the source-filter model that explains why each vowel carries its own formants, and the spectrogram the log-mel front end of every ASR system sits directly on top of — closing with neural TTS, wav2vec, HuBERT, and Whisper, where phonetics went in neural speech.\n",{"title":1285,"path":1286,"lessonNumber":994,"topics":1287,"summary":1288},"Automatic Speech Recognition","\u002Fnatural-language-processing\u002Fspeech\u002Fautomatic-speech-recognition",[1271],"Speech recognition maps an acoustic waveform to a string of words, and once the waveform is turned into a sequence of log-mel spectrogram frames the problem is the same sequence-to-sequence transduction the rest of the course already solved. This first part builds the feature front end (framing, the DFT, the mel filterbank, the log), then the modern architectures: the attention-based encoder-decoder, the CTC alignment trick that collapses repeated and blank frames, and RNN-T for streaming. Training-data advances, evaluation, TTS, and the other speech tasks come next.\n",{"title":1290,"path":1291,"lessonNumber":1000,"topics":1292,"summary":1293},"ASR Evaluation and Speech Applications","\u002Fnatural-language-processing\u002Fspeech\u002Fasr-evaluation-and-applications",[1271],"A recognizer turns a waveform into text; this part scores that text and puts the same machinery to other uses. It opens with the self-supervised and weakly- supervised systems (wav2vec 2.0, HuBERT, Whisper) that made ASR error rates fall. Word error rate reuses the edit distance from the first module, run over words. Text-to-speech runs the whole pipeline in reverse — text to mel spectrogram to waveform. And a family of smaller tasks — wake-word detection, speaker recognition and diarization, language identification — reuse the same log-mel front end without the decoder.\n",1785117679395]