{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"nltk","slug":"nltk","name":"NLTK","type":"repo","url":"https://www.nltk.org","page_url":"https://unfragile.ai/nltk","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"nltk__cap_0","uri":"capability://data.processing.analysis.language.agnostic.tokenization.with.multiple.strategies","name":"language-agnostic tokenization with multiple strategies","description":"Converts raw text into discrete token sequences using multiple tokenization strategies (word, sentence, whitespace, regex-based). NLTK provides `word_tokenize()` which handles punctuation separation, contractions, and multi-word expressions through a pre-trained punkt tokenizer model, plus customizable regex-based tokenizers for domain-specific splitting patterns. The implementation uses probabilistic sentence boundary detection rather than naive punctuation splitting, enabling accurate segmentation across 16+ languages via trained models.","intents":["I need to split raw text into words and sentences while preserving punctuation information","I want to tokenize text in multiple languages without writing custom regex patterns","I need to handle edge cases like contractions, abbreviations, and ellipses correctly"],"best_for":["NLP researchers and students building text processing pipelines","teams prototyping multilingual text analysis systems","developers building educational NLP applications"],"limitations":["Punkt sentence tokenizer requires pre-trained models (included but not customizable without retraining)","Performance degrades on noisy text (social media, OCR output) without preprocessing","No streaming tokenization — entire text must be loaded into memory","Tokenization rules are language-specific; cross-lingual text requires manual handling"],"requires":["Python 3.6+","NLTK 3.0+ with punkt tokenizer models (auto-downloaded on first use)","Text input as string or file"],"input_types":["raw text string","file path","text stream (requires manual buffering)"],"output_types":["list of token strings","list of sentence strings","token spans with character offsets (via TreebankWordTokenizer)"],"categories":["data-processing-analysis","text-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_1","uri":"capability://data.processing.analysis.part.of.speech.tagging.with.multiple.tagger.backends","name":"part-of-speech tagging with multiple tagger backends","description":"Assigns grammatical role labels (noun, verb, adjective, etc.) to tokenized words using multiple tagging algorithms. NLTK implements `pos_tag()` which defaults to the Penn Treebank tagset (45 tags) and supports pluggable backends including Hidden Markov Model (HMM) taggers, Brill transformational taggers, and pre-trained models. The framework allows training custom taggers on annotated corpora via supervised learning, enabling domain-specific POS classification without external API calls.","intents":["I need to identify the grammatical role of each word in a sentence for downstream NLP tasks","I want to train a custom POS tagger on domain-specific text with my own annotation scheme","I need to compare multiple tagging algorithms to understand their accuracy trade-offs"],"best_for":["NLP students learning tagging algorithms and their implementation","researchers experimenting with different tagger architectures","teams building domain-specific NLP pipelines (medical, legal, scientific text)"],"limitations":["Default pre-trained tagger achieves ~96% accuracy on Penn Treebank but degrades on out-of-domain text","HMM and Brill taggers require manually annotated training data (no unsupervised tagging)","No neural network-based taggers (e.g., BiLSTM) — limited to statistical models","Tagset is fixed to Penn Treebank (45 tags) or user-defined; no transfer learning from large models"],"requires":["Python 3.6+","NLTK 3.0+ with averaged_perceptron_tagger model (auto-downloaded)","Tokenized text (list of token strings)","For custom training: annotated corpus in (token, tag) tuple format"],"input_types":["list of token strings","list of (token, tag) tuples for training"],"output_types":["list of (token, POS_tag) tuples","trained tagger object (serializable)"],"categories":["data-processing-analysis","text-annotation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_10","uri":"capability://data.processing.analysis.feature.extraction.and.representation.for.machine.learning","name":"feature extraction and representation for machine learning","description":"Provides utilities for extracting features from text and representing them as dictionaries or vectors for machine learning tasks. NLTK includes functions for extracting word presence features, word frequency features, and custom feature functions, plus integration with scikit-learn for vectorization. The framework enables users to experiment with different feature representations (bag-of-words, TF-IDF, etc.) and understand their impact on classifier performance without external ML libraries.","intents":["I need to convert text into feature vectors for machine learning classification","I want to experiment with different feature representations (word presence, frequency, custom features) to improve classifier accuracy","I need to understand how feature engineering impacts text classification performance"],"best_for":["NLP students learning feature engineering and its impact on classification","teams building text classification systems with custom feature engineering","researchers experimenting with different feature representations"],"limitations":["Feature extraction is manual — requires explicit feature engineering code","No built-in support for advanced features (word embeddings, contextual features, syntactic features)","No automatic feature selection or dimensionality reduction","Feature dictionaries are sparse and memory-inefficient for large vocabularies","No support for feature interactions or polynomial features"],"requires":["Python 3.6+","NLTK 3.0+","Text data and feature extraction function"],"input_types":["text string or tokenized text","custom feature extraction function"],"output_types":["feature dictionary (e.g., {'word_great': True, 'word_count': 50})","feature vector (if integrated with scikit-learn)"],"categories":["data-processing-analysis","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_11","uri":"capability://data.processing.analysis.evaluation.metrics.and.performance.assessment.for.nlp.tasks","name":"evaluation metrics and performance assessment for nlp tasks","description":"Provides built-in evaluation metrics for assessing classifier and parser performance including precision, recall, F1-score, confusion matrices, and parsing accuracy metrics. NLTK includes `ConfusionMatrix` for classification evaluation, `accuracy()` for parser evaluation, and integration with standard metrics for comparing predicted vs. gold-standard outputs. The framework enables users to understand model performance and diagnose errors without external evaluation libraries.","intents":["I need to evaluate my text classifier's performance using standard metrics (precision, recall, F1)","I want to understand which classes my classifier confuses using a confusion matrix","I need to assess parser accuracy on test data and identify common parsing errors"],"best_for":["NLP students learning evaluation metrics and their interpretation","teams building NLP systems and assessing model performance","researchers comparing different algorithms on benchmark datasets"],"limitations":["Metrics are limited to classification and parsing — no support for generation tasks (BLEU, ROUGE, etc.)","No built-in cross-validation or statistical significance testing","Confusion matrices are difficult to interpret for large numbers of classes","No support for weighted metrics or class imbalance handling","No visualization utilities — requires external libraries for plotting"],"requires":["Python 3.6+","NLTK 3.0+","Predicted labels and gold-standard labels"],"input_types":["list of predicted labels","list of gold-standard labels"],"output_types":["confusion matrix (2D array or ConfusionMatrix object)","precision, recall, F1 scores (float)","accuracy (float)"],"categories":["data-processing-analysis","evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_12","uri":"capability://text.generation.language.educational.documentation.and.interactive.examples","name":"educational documentation and interactive examples","description":"Provides comprehensive documentation, tutorials, and interactive examples through the NLTK Book ('Natural Language Processing with Python'), API reference, and community forum. The framework includes example code for all major features, step-by-step tutorials for common NLP tasks, and a large community of educators and students. Documentation is designed for learning and understanding NLP concepts, not just API reference.","intents":["I want to learn NLP fundamentals and understand how different algorithms work","I need examples and tutorials for implementing common NLP tasks","I want to understand the theory behind NLP algorithms before implementing them"],"best_for":["NLP students and beginners learning NLP concepts and algorithms","educators teaching NLP courses using NLTK","researchers exploring NLP algorithms and their implementations"],"limitations":["Documentation is educational-focused, not production-focused — limited guidance on scaling or optimization","Examples are often simplified for clarity — may not reflect real-world complexity","Community forum has lower activity than commercial frameworks (e.g., spaCy, Hugging Face)","Documentation updates lag behind code changes"],"requires":["Python 3.6+","NLTK 3.0+","Internet access for online documentation and book"],"input_types":["none (documentation is read-only)"],"output_types":["tutorials, examples, and explanations"],"categories":["text-generation-language","education"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_2","uri":"capability://data.processing.analysis.named.entity.recognition.via.chunking.and.classification","name":"named entity recognition via chunking and classification","description":"Identifies and classifies named entities (persons, organizations, locations, etc.) in text using rule-based chunking patterns applied to POS-tagged sequences. NLTK's `chunk.ne_chunk()` function applies a pre-trained maximum entropy classifier to recognize entities, returning a nested tree structure where entities are grouped as subtrees. The implementation combines POS tags with a trained classifier, enabling both rule-based pattern matching (via `RegexpChunker`) and statistical classification without external NER models or APIs.","intents":["I need to extract and classify named entities (people, places, organizations) from unstructured text","I want to define custom entity patterns using regular expressions over POS tags","I need to understand how NER works by implementing and training my own chunker"],"best_for":["NLP students learning entity recognition and chunking algorithms","researchers building information extraction pipelines for specific domains","teams prototyping entity-based search or knowledge graph construction"],"limitations":["Pre-trained NER model recognizes only 4 entity types (PERSON, ORGANIZATION, LOCATION, GPE) — no fine-grained types","Accuracy ~85% on newswire text; degrades significantly on social media, technical, or specialized domains","Rule-based chunking requires manual pattern engineering for custom entity types","No support for nested entities or overlapping entity spans","Requires POS tagging as prerequisite — errors propagate through the pipeline"],"requires":["Python 3.6+","NLTK 3.0+ with maxent_ne_chunker model (auto-downloaded)","POS-tagged text (output from pos_tag())","For custom chunking: regex patterns over POS tag sequences"],"input_types":["list of (token, POS_tag) tuples","regex patterns for RegexpChunker"],"output_types":["Tree structure with entity subtrees","flattened list of (entity_text, entity_type) tuples (via tree traversal)"],"categories":["data-processing-analysis","information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_3","uri":"capability://data.processing.analysis.syntactic.parsing.with.context.free.grammar.trees","name":"syntactic parsing with context-free grammar trees","description":"Constructs hierarchical parse trees representing the grammatical structure of sentences using context-free grammar (CFG) rules. NLTK provides `ChartParser` and `RecursiveDescentParser` implementations that apply user-defined grammar rules to tokenized and tagged text, returning Tree objects that encode phrase structure (NP, VP, S, etc.). The framework includes pre-trained parsers trained on the Penn Treebank corpus and allows users to define custom grammars for domain-specific parsing without external parsing services.","intents":["I need to understand the grammatical structure of sentences for semantic analysis or information extraction","I want to define custom grammar rules for domain-specific language parsing","I need to extract noun phrases, verb phrases, or other syntactic constituents from text"],"best_for":["NLP students learning parsing algorithms and grammar-based language analysis","researchers building domain-specific parsers (e.g., for programming languages, configuration files)","teams extracting structured information from text via syntactic patterns"],"limitations":["Pre-trained parser achieves ~88% F1 on Penn Treebank but requires extensive training data","No dependency parsing — only constituency parsing (phrase structure trees)","Parsing is computationally expensive (~1-5 seconds per sentence for complex grammars)","No support for ambiguity resolution — returns all possible parses (exponential in sentence length)","Grammar rules must be manually defined or trained; no automatic grammar induction"],"requires":["Python 3.6+","NLTK 3.0+","POS-tagged text (output from pos_tag())","Context-free grammar rules in NLTK format or pre-trained parser model"],"input_types":["list of (token, POS_tag) tuples","CFG rule strings (e.g., 'NP -> DET ADJ NOUN')"],"output_types":["Tree objects (nested structure representing parse tree)","multiple Tree objects if grammar is ambiguous","extracted subtrees (e.g., all NP nodes)"],"categories":["data-processing-analysis","syntactic-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_4","uri":"capability://data.processing.analysis.text.classification.with.supervised.learning.algorithms","name":"text classification with supervised learning algorithms","description":"Trains and applies machine learning classifiers to categorize text into predefined categories using feature extraction and supervised learning. NLTK provides `NaiveBayesClassifier`, `DecisionTreeClassifier`, and `MaxentClassifier` implementations that accept feature dictionaries (extracted from text) and class labels, returning trained classifiers with prediction and probability estimation methods. The framework includes utilities for feature engineering (e.g., extracting word presence, frequency, or custom features) and evaluation metrics (precision, recall, F1) for assessing classifier performance.","intents":["I need to classify documents or sentences into predefined categories (sentiment, topic, spam, etc.)","I want to train a custom classifier on my own labeled dataset without external ML services","I need to understand how different classification algorithms perform on my data"],"best_for":["NLP students learning supervised classification and feature engineering","teams building text categorization systems for specific domains (sentiment analysis, spam detection, topic classification)","researchers experimenting with different classifier architectures on small-to-medium datasets"],"limitations":["Classifiers are shallow learners — no deep neural networks or transfer learning","Feature engineering is manual — requires explicit feature extraction code","Naive Bayes assumes feature independence (unrealistic for text)","No built-in cross-validation or hyperparameter tuning","Scalability limited to datasets that fit in memory; no distributed training","No support for multi-label classification or hierarchical categories"],"requires":["Python 3.6+","NLTK 3.0+","Labeled training data as list of (feature_dict, label) tuples","Feature extraction function to convert text to feature dictionaries"],"input_types":["feature dictionary (e.g., {'contains_word_great': True, 'word_count': 50})","list of (feature_dict, label) tuples for training"],"output_types":["predicted class label (string)","probability distribution over classes (dict)","trained classifier object (serializable)"],"categories":["data-processing-analysis","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_5","uri":"capability://memory.knowledge.corpus.access.and.management.with.50.built.in.datasets","name":"corpus access and management with 50+ built-in datasets","description":"Provides programmatic access to 50+ pre-downloaded linguistic corpora and lexical resources (WordNet, Brown Corpus, Penn Treebank, etc.) via a unified API. NLTK's `nltk.corpus` module exposes corpora as Python objects with methods for iterating over sentences, words, tagged sequences, and parse trees without manual file parsing. The framework handles corpus downloading, caching, and format conversion transparently, enabling researchers to focus on analysis rather than data engineering.","intents":["I need to access standard linguistic corpora (Brown, Penn Treebank, etc.) for training or evaluation","I want to explore linguistic patterns in large text collections without writing file parsing code","I need to use WordNet for semantic analysis, synonym lookup, or word sense disambiguation"],"best_for":["NLP students and researchers using standard benchmarks for algorithm development","teams building educational NLP applications with reference datasets","linguists analyzing linguistic patterns across multiple corpora"],"limitations":["Corpora are static snapshots — no real-time or streaming data","Corpus sizes are modest by modern standards (largest ~1M words) — insufficient for training modern NLP models","Corpora are primarily English-focused; limited multilingual coverage","Download on first use adds latency (~100MB+ of data); requires internet connection","No built-in corpus versioning or update mechanism","WordNet coverage is limited to English; no support for other languages"],"requires":["Python 3.6+","NLTK 3.0+","Internet connection for first-time corpus download","~500MB disk space for all corpora (or selective download)"],"input_types":["corpus name (string, e.g., 'brown', 'treebank')","word or phrase for WordNet lookup"],"output_types":["list of sentences (list of token lists)","list of words (flat list of strings)","list of tagged sequences (list of (token, tag) tuples)","parse trees (Tree objects)","WordNet synsets and lemmas (Synset and Lemma objects)"],"categories":["memory-knowledge","data-access"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_6","uri":"capability://data.processing.analysis.stemming.and.lemmatization.for.word.normalization","name":"stemming and lemmatization for word normalization","description":"Reduces words to their root forms using rule-based stemming or dictionary-based lemmatization. NLTK provides `PorterStemmer` (rule-based suffix stripping for English), `SnowballStemmer` (multilingual stemming for 15+ languages), and `WordNetLemmatizer` (dictionary-based lemmatization using WordNet). Stemming applies algorithmic rules to strip suffixes, while lemmatization uses a lexical database to map words to canonical forms, enabling text normalization for downstream tasks like clustering or information retrieval.","intents":["I need to normalize words to their root forms to improve text clustering or search recall","I want to reduce vocabulary size by conflating morphological variants (e.g., 'running', 'runs', 'ran' → 'run')","I need to apply stemming or lemmatization in multiple languages"],"best_for":["NLP students learning morphological analysis and word normalization","teams building search engines or information retrieval systems","researchers reducing vocabulary size for text classification or clustering"],"limitations":["Porter Stemmer is rule-based and produces non-words (e.g., 'ponies' → 'poni'); over-stems in some cases","Lemmatization requires POS tags for accuracy — errors propagate from tagger","Snowball Stemmer coverage is limited to 15 languages; no support for morphologically complex languages","No support for domain-specific stemming rules","Stemming/lemmatization can harm semantic precision (e.g., 'universal' and 'university' both stem to 'univers')"],"requires":["Python 3.6+","NLTK 3.0+","For lemmatization: WordNet corpus (auto-downloaded)","For lemmatization: POS tags (output from pos_tag())"],"input_types":["word string (for stemming)","list of (token, POS_tag) tuples (for lemmatization)"],"output_types":["stem string (e.g., 'run')","lemma string (e.g., 'run')"],"categories":["data-processing-analysis","text-normalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_7","uri":"capability://memory.knowledge.semantic.similarity.and.word.sense.disambiguation.via.wordnet","name":"semantic similarity and word sense disambiguation via wordnet","description":"Measures semantic similarity between words and disambiguates word senses using WordNet's hierarchical structure of synsets (synonym sets). NLTK provides methods like `path_similarity()`, `lch_similarity()`, and `wup_similarity()` that compute similarity scores based on the shortest path between synsets in the WordNet hierarchy, plus `lesk()` for word sense disambiguation using context. The implementation enables semantic reasoning without external knowledge bases or embedding models, relying on manually curated lexical relationships.","intents":["I need to measure semantic similarity between words for paraphrase detection or synonym expansion","I want to disambiguate word senses in context (e.g., 'bank' as financial institution vs. river bank)","I need to find synonyms, antonyms, or hypernyms for a word"],"best_for":["NLP students learning semantic analysis and word sense disambiguation","teams building question-answering or paraphrase detection systems","researchers exploring lexical semantics without embedding models"],"limitations":["WordNet coverage is limited to English; no support for other languages","Similarity scores are based on path distance in hierarchy — not grounded in corpus statistics","Lesk algorithm for WSD is simplistic (bag-of-words overlap) — ~55-60% accuracy on standard benchmarks","WordNet is manually curated and incomplete — missing many modern words, slang, and technical terms","No support for polysemy or fine-grained sense distinctions","Similarity scores are not comparable across different word pairs (no normalization)"],"requires":["Python 3.6+","NLTK 3.0+ with WordNet corpus (auto-downloaded)","Word strings or synsets for similarity computation"],"input_types":["word string (e.g., 'dog')","synset objects (e.g., wordnet.synset('dog.n.01'))","context (list of words for WSD)"],"output_types":["similarity score (float, 0-1)","synset object (for WSD)","list of synsets (for sense enumeration)"],"categories":["memory-knowledge","semantic-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_8","uri":"capability://data.processing.analysis.frequency.analysis.and.collocation.extraction","name":"frequency analysis and collocation extraction","description":"Identifies frequently occurring words, n-grams, and collocations (word pairs that co-occur more often than chance) in text corpora. NLTK provides `FreqDist` for word frequency analysis, `BigramCollocationFinder` and `TrigramCollocationFinder` for extracting significant collocations using statistical measures (PMI, likelihood ratio, chi-square), and `ConditionalFreqDist` for analyzing frequency distributions conditioned on categories. The implementation enables corpus-based linguistic analysis without external statistical libraries.","intents":["I need to identify the most frequent words or n-grams in a text corpus","I want to find statistically significant word pairs (collocations) that appear together more often than expected","I need to analyze word frequency distributions across different categories or time periods"],"best_for":["NLP students learning corpus linguistics and statistical analysis","linguists analyzing language patterns and word associations","teams building vocabulary lists or identifying domain-specific terminology"],"limitations":["Collocation detection requires large corpora (minimum ~100K words) for statistical significance","No built-in visualization — requires external libraries (matplotlib, etc.)","Statistical measures (PMI, likelihood ratio) assume independence — may not capture semantic relationships","No support for context-dependent collocations (e.g., collocations specific to certain syntactic positions)","Frequency analysis is case-sensitive and punctuation-sensitive — requires preprocessing"],"requires":["Python 3.6+","NLTK 3.0+","Tokenized text (list of token lists or flat list of tokens)"],"input_types":["list of tokens (for FreqDist)","list of token lists (for BigramCollocationFinder)","list of (category, token) tuples (for ConditionalFreqDist)"],"output_types":["frequency distribution object (FreqDist) with methods for top-N, probability, etc.","list of significant collocations (tuple pairs with scores)","conditional frequency distribution (ConditionalFreqDist)"],"categories":["data-processing-analysis","statistical-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__cap_9","uri":"capability://data.processing.analysis.custom.grammar.definition.and.parsing.with.context.free.grammars","name":"custom grammar definition and parsing with context-free grammars","description":"Allows users to define custom context-free grammar (CFG) rules in NLTK syntax and apply them to parse text using multiple parsing algorithms. NLTK provides `CFG.fromstring()` for defining grammars, `ChartParser` for efficient bottom-up parsing, and `RecursiveDescentParser` for top-down parsing. Users can define domain-specific grammar rules (e.g., for configuration files, programming languages, or specialized text formats) and test them on custom data without external parsing tools.","intents":["I need to parse domain-specific text formats (e.g., configuration files, log files) using custom grammar rules","I want to understand how parsing algorithms work by implementing and testing custom grammars","I need to extract structured information from text using grammar-based patterns"],"best_for":["NLP students learning parsing algorithms and grammar-based language analysis","teams building domain-specific parsers for specialized text formats","researchers experimenting with grammar-based information extraction"],"limitations":["Grammar rules must be manually defined — no automatic grammar induction","Parsing is computationally expensive for large grammars or long sentences (exponential in worst case)","No support for ambiguity resolution — returns all possible parses","Context-free grammars cannot express context-sensitive phenomena (e.g., agreement, long-distance dependencies)","No support for probabilistic grammars (weighted rules)","Debugging grammar errors is difficult — no error messages or suggestions"],"requires":["Python 3.6+","NLTK 3.0+","Grammar rules in NLTK CFG format (e.g., 'NP -> DET ADJ NOUN')","Tokenized and POS-tagged text (or custom tokens matching grammar terminals)"],"input_types":["CFG rule strings","list of (token, POS_tag) tuples for parsing"],"output_types":["Tree objects (parse trees)","multiple Tree objects if grammar is ambiguous"],"categories":["data-processing-analysis","parsing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nltk__headline","uri":"capability://data.processing.analysis.natural.language.processing.toolkit","name":"natural language processing toolkit","description":"NLTK is a comprehensive library for natural language processing in Python, offering tools for tokenization, tagging, parsing, and classification, making it ideal for educational and research purposes in NLP.","intents":["best NLP library","NLP toolkit for text processing","top tools for natural language processing","best libraries for NLP research","NLP solutions for Python developers"],"best_for":["educational purposes","research projects","text analysis tasks"],"limitations":["not optimized for large datasets","limited advanced features"],"requires":["Python environment"],"input_types":["text"],"output_types":["tokens","tags","parse trees"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+","NLTK 3.0+ with punkt tokenizer models (auto-downloaded on first use)","Text input as string or file","NLTK 3.0+ with averaged_perceptron_tagger model (auto-downloaded)","Tokenized text (list of token strings)","For custom training: annotated corpus in (token, tag) tuple format","NLTK 3.0+","Text data and feature extraction function","Predicted labels and gold-standard labels","Internet access for online documentation and book"],"failure_modes":["Punkt sentence tokenizer requires pre-trained models (included but not customizable without retraining)","Performance degrades on noisy text (social media, OCR output) without preprocessing","No streaming tokenization — entire text must be loaded into memory","Tokenization rules are language-specific; cross-lingual text requires manual handling","Default pre-trained tagger achieves ~96% accuracy on Penn Treebank but degrades on out-of-domain text","HMM and Brill taggers require manually annotated training data (no unsupervised tagging)","No neural network-based taggers (e.g., BiLSTM) — limited to statistical models","Tagset is fixed to Penn Treebank (45 tags) or user-defined; no transfer learning from large models","Feature extraction is manual — requires explicit feature engineering code","No built-in support for advanced features (word embeddings, contextual features, syntactic features)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:23.328Z","last_scraped_at":null,"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=nltk","compare_url":"https://unfragile.ai/compare?artifact=nltk"}},"signature":"adNxU1oBHPFlcbbkBifOo8Tk77LopZJzItHPGC4/svTkZfX+PZFgmiWah39jGbsjUxyGFLSdRyrDZMal5c4nAg==","signedAt":"2026-06-22T03:54:42.999Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nltk","artifact":"https://unfragile.ai/nltk","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}