{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-nltk","slug":"pypi-nltk","name":"nltk","type":"repo","url":"https://www.nltk.org/","page_url":"https://unfragile.ai/pypi-nltk","categories":["frameworks-sdks"],"tags":["NLP","CL","natural","language","processing","computational","linguistics","parsing","tagging","tokenizing","syntax","linguistics","language","natural","language","text","analytics"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-nltk__cap_0","uri":"capability://data.processing.analysis.multilingual.word.and.sentence.tokenization.with.contraction.handling","name":"multilingual word and sentence tokenization with contraction handling","description":"Splits raw text into word tokens and sentences using language-specific regex patterns and punkt sentence segmentation models. Handles edge cases like contractions ('didn't' → 'did', 'n't'), abbreviations, and punctuation via trained statistical models rather than simple whitespace splitting. The `nltk.word_tokenize()` function applies Penn Treebank tokenization conventions, preserving linguistic structure needed for downstream NLP tasks.","intents":["I need to split raw text into individual words and sentences for further linguistic analysis","I want to handle contractions and abbreviations correctly without manual regex rules","I need tokenization that respects linguistic conventions like Penn Treebank standards"],"best_for":["NLP researchers and students building text processing pipelines","developers prototyping linguistic analysis tools without deep learning infrastructure","teams needing rule-based tokenization with educational transparency"],"limitations":["Punkt sentence segmentation is trained on English; multilingual support requires separate models","Contraction handling is English-centric (e.g., 'n't splitting); other languages may tokenize incorrectly","No streaming/online tokenization — requires full text in memory","Performance degrades on very long documents (>1M tokens) due to regex-based approach"],"requires":["Python 3.6+","NLTK package installed via pip","NLTK data downloaded via `nltk.download('punkt')` for sentence segmentation models"],"input_types":["raw text strings","unicode text with mixed languages"],"output_types":["list of token strings","list of sentence strings"],"categories":["data-processing-analysis","text-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_1","uri":"capability://data.processing.analysis.part.of.speech.tagging.with.penn.treebank.tagset","name":"part-of-speech tagging with penn treebank tagset","description":"Assigns grammatical tags (NN, VB, JJ, IN, etc.) to tokenized words using a pre-trained averaged perceptron model trained on Penn Treebank corpus. The `nltk.pos_tag()` function takes a list of tokens and returns tuples of (word, tag) pairs. Internally uses a statistical classifier that learns tag sequences from annotated training data, enabling context-aware tagging (e.g., 'bank' tagged as NN vs VB depending on surrounding words).","intents":["I need to identify the grammatical role of each word in a sentence (noun, verb, adjective, etc.)","I want to use POS tags as features for downstream NLP tasks like named entity recognition or parsing","I need a pre-trained tagger that works out-of-the-box without training data"],"best_for":["NLP students learning linguistic annotation and grammar","developers building rule-based information extraction systems","researchers prototyping syntax-aware text analysis without deep learning"],"limitations":["Pre-trained model is English-only; other languages require separate trained models or custom training","Accuracy ~97% on Penn Treebank test set but degrades on out-of-domain text (e.g., social media, technical jargon)","Tagset is Penn Treebank (45 tags); not compatible with other tagsets (e.g., Universal Dependencies) without conversion","No confidence scores or alternative tag hypotheses — returns single best tag per word","Requires tokenized input; errors in tokenization propagate to tagging"],"requires":["Python 3.6+","NLTK package installed","NLTK data downloaded via `nltk.download('averaged_perceptron_tagger')` for pre-trained model","Pre-tokenized input (list of strings)"],"input_types":["list of token strings (output from nltk.word_tokenize or equivalent)"],"output_types":["list of (word, tag) tuples where tag is a Penn Treebank tag (NN, VB, JJ, IN, etc.)"],"categories":["data-processing-analysis","text-annotation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_10","uri":"capability://data.processing.analysis.semantic.role.labeling.and.predicate.argument.structure.extraction","name":"semantic role labeling and predicate-argument structure extraction","description":"Extracts semantic roles (Agent, Patient, Instrument, etc.) and predicate-argument structures from parsed sentences. NLTK provides tools for analyzing semantic relationships beyond syntactic structure, enabling developers to identify 'who did what to whom' in sentences. Uses parse trees and semantic role annotations from corpora to extract structured semantic information.","intents":["I need to extract predicate-argument structures (who did what to whom) from sentences","I want to identify semantic roles (Agent, Patient, Instrument) for information extraction","I need to analyze semantic relationships beyond syntactic structure"],"best_for":["NLP researchers studying semantic role labeling and argument structure","developers building information extraction systems for structured data","teams analyzing semantic relationships in text"],"limitations":["Semantic role labeling requires pre-annotated corpora; no automatic SRL without training data","SRL accuracy is lower than modern neural SRL systems","Limited to predefined semantic role inventories (PropBank, FrameNet); no custom role definitions","Requires syntactic parse trees; errors in parsing degrade SRL performance","No support for implicit arguments or dropped pronouns"],"requires":["Python 3.6+","NLTK package installed","NLTK data downloaded for semantic role resources (if available)","Pre-parsed sentences or parse trees"],"input_types":["parse trees (nltk.Tree objects)","pre-annotated semantic role data"],"output_types":["semantic role annotations","predicate-argument structures"],"categories":["data-processing-analysis","semantic-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_11","uri":"capability://planning.reasoning.feature.based.decision.tree.and.maximum.entropy.classification","name":"feature-based decision tree and maximum entropy classification","description":"Trains and applies feature-based classifiers using decision trees and maximum entropy models via the `nltk.classify` module. Developers define custom feature extraction functions, then train classifiers on labeled datasets. Decision trees provide interpretable rules (e.g., 'if word contains \"not\" then negative'), while maximum entropy models learn probabilistic feature weights. Both classifiers support `.classify()` for prediction and `.show_most_informative_features()` for interpretability.","intents":["I need to train interpretable classifiers with explicit decision rules","I want to understand which features drive classification decisions","I need probabilistic predictions with confidence scores"],"best_for":["NLP students learning classification algorithms and feature engineering","developers building interpretable classifiers for regulatory or high-stakes applications","researchers comparing classification approaches"],"limitations":["Decision trees are prone to overfitting; require manual pruning or regularization","Maximum entropy models are slower to train than naive Bayes","No built-in cross-validation, hyperparameter tuning, or regularization","Feature extraction is manual; no automatic feature learning","Scalability is limited; training on large datasets is slow","No support for deep learning or neural architectures"],"requires":["Python 3.6+","NLTK package installed","Labeled training data","Custom feature extraction function"],"input_types":["feature dictionaries","labeled training data"],"output_types":["predicted class labels","feature importance rankings","decision rules (for decision trees)"],"categories":["planning-reasoning","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_2","uri":"capability://data.processing.analysis.named.entity.recognition.via.chunking.with.tree.based.output","name":"named entity recognition via chunking with tree-based output","description":"Identifies and classifies named entities (PERSON, ORGANIZATION, LOCATION, etc.) in POS-tagged text by applying a pre-trained chunker that wraps entities in nested tree structures. The `nltk.chunk.ne_chunk()` function takes POS-tagged sequences and returns an `nltk.Tree` object where entity spans are nested as subtrees labeled with entity types. Uses a maximum entropy classifier trained on the ACE corpus to recognize entity boundaries and types based on word, POS tag, and context features.","intents":["I need to extract and classify named entities (people, organizations, locations) from text","I want entity boundaries and types in a structured tree format for downstream processing","I need a pre-trained NER system that works without training data or external APIs"],"best_for":["NLP researchers building information extraction pipelines","developers prototyping entity-aware text analysis without cloud dependencies","students learning named entity recognition and linguistic annotation"],"limitations":["Pre-trained model recognizes only 3 entity types (PERSON, ORGANIZATION, LOCATION); no fine-grained types (e.g., PRODUCT, EVENT)","Accuracy is lower than modern neural NER systems (~85% F1 vs 90%+ for transformer-based models)","Requires POS-tagged input; errors in POS tagging degrade NER performance","No confidence scores or alternative entity hypotheses","English-only; multilingual NER requires separate models or custom training","Tree-based output requires tree traversal for entity extraction; not directly compatible with standard NER output formats (e.g., BIO tags)"],"requires":["Python 3.6+","NLTK package installed","NLTK data downloaded via `nltk.download('maxent_ne_chunker')` and `nltk.download('words')` for pre-trained model and word lists","POS-tagged input (output from nltk.pos_tag or equivalent)"],"input_types":["list of (word, pos_tag) tuples from POS tagging"],"output_types":["nltk.Tree object with entity spans as nested subtrees labeled with entity types (PERSON, ORGANIZATION, LOCATION)"],"categories":["data-processing-analysis","information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_3","uri":"capability://data.processing.analysis.syntactic.parse.tree.construction.and.visualization","name":"syntactic parse tree construction and visualization","description":"Constructs and visualizes hierarchical parse trees representing the grammatical structure of sentences. NLTK provides access to pre-parsed corpora (e.g., Penn Treebank via `nltk.corpus.treebank.parsed_sents()`) and includes parsers for generating new parse trees from raw text. The `Tree` class represents parse trees as nested structures where each node is labeled with a syntactic category (S, NP, VP, etc.) and leaf nodes are words. The `.draw()` method renders trees graphically, enabling visual inspection of sentence structure.","intents":["I need to analyze the grammatical structure of sentences for linguistic research","I want to visualize parse trees to understand sentence syntax","I need to extract syntactic constituents (noun phrases, verb phrases) from sentences"],"best_for":["computational linguists and syntax researchers","NLP students learning grammar and syntactic analysis","developers building syntax-aware information extraction systems"],"limitations":["Pre-parsed corpora are limited to Penn Treebank and similar resources; parsing new text requires a separate parser (not included in core NLTK)","Tree visualization requires a graphical display (e.g., Jupyter notebook or X11 window); not suitable for headless/server environments","Penn Treebank trees use constituency parsing (phrase-structure); dependency parsing requires separate tools or conversion","No support for semantic role labeling or other semantic annotations beyond syntactic structure","Tree traversal and manipulation require manual recursive algorithms; no built-in query language (e.g., XPath)"],"requires":["Python 3.6+","NLTK package installed","NLTK data downloaded via `nltk.download('treebank')` for pre-parsed corpus","Graphical display capability for tree visualization (e.g., Jupyter, X11, or PIL/Tkinter)"],"input_types":["pre-parsed tree files (Penn Treebank .mrg format)","nltk.Tree objects"],"output_types":["nltk.Tree objects (nested structures with syntactic labels)","graphical tree visualizations"],"categories":["data-processing-analysis","syntax-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_4","uri":"capability://memory.knowledge.unified.corpus.and.lexical.resource.access.with.lazy.loading","name":"unified corpus and lexical resource access with lazy loading","description":"Provides a unified Python interface to 50+ linguistic corpora and lexical resources (e.g., Penn Treebank, WordNet, Brown Corpus) via the `nltk.corpus` module. Corpora are accessed as Python objects with methods like `.words()`, `.sents()`, `.parsed_sents()`, enabling lazy loading of data on-demand rather than loading entire corpora into memory. The abstraction handles file I/O, format parsing (.mrg, .txt, etc.), and caching, allowing developers to access diverse linguistic resources with consistent APIs.","intents":["I need access to standard linguistic corpora for training, evaluation, or analysis without managing file formats","I want to load corpus data on-demand without pre-loading entire datasets into memory","I need a consistent Python API across diverse corpus formats and sources"],"best_for":["NLP researchers and students working with standard corpora","developers building corpus-based NLP systems without custom data pipelines","teams prototyping linguistic analysis without external data infrastructure"],"limitations":["Corpus selection is fixed to NLTK's curated set; adding custom corpora requires manual integration or subclassing","Lazy loading has overhead for first access; repeated access may be slower than pre-loaded data structures","Corpora are English-centric; multilingual resources are limited","No built-in filtering, sampling, or stratification; requires manual iteration for subset selection","Corpus data is not versioned; updates to NLTK data may change corpus contents unexpectedly"],"requires":["Python 3.6+","NLTK package installed","NLTK data downloaded via `nltk.download()` for specific corpora (e.g., `nltk.download('treebank')`, `nltk.download('wordnet')`)"],"input_types":["corpus identifiers (strings like 'treebank', 'brown', 'wordnet')"],"output_types":["lists of words, sentences, or parse trees depending on corpus type","WordNet synset objects and lexical relations"],"categories":["memory-knowledge","data-access"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_5","uri":"capability://data.processing.analysis.stemming.and.lemmatization.with.multiple.algorithm.options","name":"stemming and lemmatization with multiple algorithm options","description":"Reduces words to their root forms using rule-based stemming algorithms (Porter Stemmer, Snowball) or lemmatization via WordNet. Stemming applies morphological rules to strip affixes (e.g., 'running' → 'run', 'happiness' → 'happi'), while lemmatization uses lexical databases to find canonical forms (e.g., 'better' → 'good'). NLTK provides multiple stemmer implementations (PorterStemmer, SnowballStemmer for 15+ languages) and WordNet-based lemmatization, enabling developers to choose trade-offs between speed, accuracy, and language coverage.","intents":["I need to normalize words to their root forms for text classification or clustering","I want to reduce vocabulary size and improve feature representation for downstream models","I need stemming or lemmatization in multiple languages"],"best_for":["NLP practitioners building text classification and clustering systems","developers reducing vocabulary size for machine learning models","researchers comparing stemming vs lemmatization approaches"],"limitations":["Porter Stemmer uses rule-based morphology; produces non-words (e.g., 'happi' for 'happiness') unsuitable for human-readable output","Lemmatization requires POS tags for accurate results; errors in POS tagging degrade lemmatization","Snowball Stemmer supports 15+ languages but quality varies; non-English stemmers may be less accurate than English","No support for irregular morphology (e.g., 'went' → 'go') without manual rules","Stemming/lemmatization may conflate semantically distinct words (e.g., 'universal' and 'university' both stem to 'univers')"],"requires":["Python 3.6+","NLTK package installed","NLTK data downloaded via `nltk.download('wordnet')` for lemmatization","POS-tagged input for accurate lemmatization (optional but recommended)"],"input_types":["word strings or lists of words","(word, pos_tag) tuples for lemmatization"],"output_types":["stemmed or lemmatized word strings"],"categories":["data-processing-analysis","text-normalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_6","uri":"capability://planning.reasoning.text.classification.with.naive.bayes.and.custom.feature.extraction","name":"text classification with naive bayes and custom feature extraction","description":"Trains and applies text classifiers using naive Bayes and other statistical models via the `nltk.classify` module. Developers define custom feature extraction functions that map text to feature dictionaries (e.g., presence of specific words, n-grams, POS tags), then train classifiers on labeled datasets. The module provides `NaiveBayesClassifier.train()` for training and `.classify()` for prediction, with built-in accuracy evaluation and feature importance analysis via `.show_most_informative_features()`.","intents":["I need to train a text classifier on labeled data without external ML libraries","I want to understand which features (words, patterns) drive classification decisions","I need a simple, interpretable classifier for sentiment analysis, topic classification, or similar tasks"],"best_for":["NLP students learning text classification and feature engineering","developers building interpretable classifiers for low-stakes applications","researchers prototyping feature-based classification approaches"],"limitations":["Naive Bayes assumes feature independence, which is violated in natural language; accuracy is lower than modern neural classifiers","Feature extraction is manual; developers must design features; no automatic feature learning","No support for deep learning or neural architectures","Scalability is limited; training on large datasets (>1M examples) is slow","No built-in cross-validation, hyperparameter tuning, or regularization","Requires labeled training data; no semi-supervised or transfer learning support"],"requires":["Python 3.6+","NLTK package installed","Labeled training data (list of (text, label) tuples)","Custom feature extraction function"],"input_types":["text strings or feature dictionaries","labeled training data as (features, label) tuples"],"output_types":["predicted class labels","feature importance rankings"],"categories":["planning-reasoning","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_7","uri":"capability://memory.knowledge.semantic.similarity.and.relatedness.via.wordnet","name":"semantic similarity and relatedness via wordnet","description":"Computes semantic similarity and relatedness between words using WordNet, a lexical database of English words organized into synsets (synonym sets) and hypernym/hyponym relations. The `nltk.corpus.wordnet` module provides methods like `.path_similarity()`, `.lch_similarity()`, and `.wup_similarity()` that measure distance between synsets based on their position in the WordNet hierarchy. Enables developers to find synonyms, antonyms, and semantically related words without external APIs or pre-trained embeddings.","intents":["I need to find synonyms and semantically related words for a given word","I want to measure semantic similarity between words for text analysis or information retrieval","I need to expand queries or text with semantically related terms"],"best_for":["NLP researchers studying lexical semantics and word relationships","developers building query expansion or synonym detection systems","teams building semantic search without embedding models"],"limitations":["WordNet is English-only; no support for other languages","Similarity metrics are based on taxonomy distance, not distributional semantics; may miss contextual similarity (e.g., 'bank' and 'river' are not related in WordNet)","WordNet has limited coverage of modern slang, technical jargon, and proper nouns","Similarity scores are not comparable across different metric types (path_similarity vs wup_similarity)","No support for word senses beyond WordNet's inventory; polysemy handling is limited","Performance degrades for rare or out-of-vocabulary words"],"requires":["Python 3.6+","NLTK package installed","NLTK data downloaded via `nltk.download('wordnet')`"],"input_types":["word strings","WordNet synset objects"],"output_types":["similarity scores (floats between 0 and 1)","lists of synonyms, antonyms, or related words"],"categories":["memory-knowledge","semantic-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_8","uri":"capability://data.processing.analysis.n.gram.generation.and.frequency.analysis","name":"n-gram generation and frequency analysis","description":"Generates n-grams (sequences of n consecutive tokens) from text and analyzes their frequency distributions. The `nltk.util.ngrams()` function produces all n-grams of a specified length from a token sequence, while `nltk.FreqDist()` computes frequency distributions of n-grams or other linguistic units. Enables developers to identify common word sequences, collocations, and patterns for language modeling, feature extraction, or linguistic analysis.","intents":["I need to extract common word sequences (bigrams, trigrams) from text","I want to analyze frequency distributions of n-grams for language modeling or feature engineering","I need to identify collocations and common phrases in a corpus"],"best_for":["NLP researchers analyzing language patterns and collocations","developers building language models or text generation systems","teams extracting features for text classification or clustering"],"limitations":["No built-in collocation detection or statistical significance testing; requires manual filtering","Frequency analysis is in-memory; large corpora may exceed memory limits","No support for skip-grams or other advanced n-gram variants","N-gram generation is naive; no handling of sentence boundaries or special tokens","Frequency distributions are unsmoothed; rare n-grams have zero probability"],"requires":["Python 3.6+","NLTK package installed","Tokenized input (list of tokens)"],"input_types":["lists of tokens","lists of words or other linguistic units"],"output_types":["lists of n-gram tuples","FreqDist objects (frequency distributions)"],"categories":["data-processing-analysis","statistical-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-nltk__cap_9","uri":"capability://search.retrieval.concordance.and.keyword.in.context.search","name":"concordance and keyword-in-context search","description":"Searches for word occurrences in text and displays them in context via concordance views. The `nltk.Text` class wraps a token list and provides `.concordance()` method to find all occurrences of a word and display surrounding context (typically 25 characters on each side). Enables developers and researchers to explore word usage patterns, collocations, and semantic contexts without manual text inspection.","intents":["I need to find all occurrences of a word in a corpus and see its surrounding context","I want to analyze how a word is used in different contexts","I need to explore word usage patterns for linguistic research or corpus analysis"],"best_for":["linguists and corpus researchers studying word usage","students learning corpus linguistics and concordance analysis","developers building corpus exploration tools"],"limitations":["Concordance output is text-based; no structured data export (e.g., JSON, CSV)","Context window is fixed (25 characters); no customization","No support for phrase or pattern-based search; only exact word matching","Concordance generation is slow for large corpora (>1M tokens)","No filtering or sorting options (e.g., by frequency, context type)"],"requires":["Python 3.6+","NLTK package installed","nltk.Text object created from token list"],"input_types":["word strings","nltk.Text objects (token lists)"],"output_types":["text-based concordance output (printed to console or returned as string)"],"categories":["search-retrieval","corpus-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+","NLTK package installed via pip","NLTK data downloaded via `nltk.download('punkt')` for sentence segmentation models","NLTK package installed","NLTK data downloaded via `nltk.download('averaged_perceptron_tagger')` for pre-trained model","Pre-tokenized input (list of strings)","NLTK data downloaded for semantic role resources (if available)","Pre-parsed sentences or parse trees","Labeled training data","Custom feature extraction function"],"failure_modes":["Punkt sentence segmentation is trained on English; multilingual support requires separate models","Contraction handling is English-centric (e.g., 'n't splitting); other languages may tokenize incorrectly","No streaming/online tokenization — requires full text in memory","Performance degrades on very long documents (>1M tokens) due to regex-based approach","Pre-trained model is English-only; other languages require separate trained models or custom training","Accuracy ~97% on Penn Treebank test set but degrades on out-of-domain text (e.g., social media, technical jargon)","Tagset is Penn Treebank (45 tags); not compatible with other tagsets (e.g., Universal Dependencies) without conversion","No confidence scores or alternative tag hypotheses — returns single best tag per word","Requires tokenized input; errors in tokenization propagate to tagging","Semantic role labeling requires pre-annotated corpora; no automatic SRL without training data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:25.058Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-nltk","compare_url":"https://unfragile.ai/compare?artifact=pypi-nltk"}},"signature":"yz+dy7spBGgiz8iZbZC1r6NuHRirf5Dc3Xh357jelAMd7nnx19wizGwQOMHTghowae3epW2ZydowuwtO7emACw==","signedAt":"2026-06-22T13:56:02.355Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-nltk","artifact":"https://unfragile.ai/pypi-nltk","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-nltk","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}