{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/17"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"Parses unstructured person and company names into labeled components using probabilistic NLP methods, handling complex cases where rule-based parsing typically fails.","skillfed_tags":["name-parsing","nlp","data-cleaning"],"use_cases":["Clean and standardize person names from user-submitted forms or data imports before storage or matching","Extract and categorize company legal entity types (Inc, LLC, Corp) from unstructured business name strings","Deduplicate or link records by parsing names into components and comparing them structurally","Prepare name data for downstream NLP or machine learning pipelines that require labeled name parts","Build data quality checks that flag names the parser cannot confidently decompose into standard components"],"what_it_does":"probablepeople is a Python library that breaks down person and company names from unstructured text into their semantic components\u2014given names, surnames, prefixes, suffixes, corporation types, and so on\u2014using conditional random fields trained on labeled examples. It sits on top of the parserator framework and python-crfsuite, learning patterns from training data rather than relying on rigid rules. The library exposes two main methods: `parse` splits a name string into tuples of (text, label), while `tag` merges consecutive components and returns an ordered dictionary keyed by component type plus an inferred entity type (Person or Corporation).\n\nThe package is designed for cases where simple regex or dictionary-based parsing breaks down\u2014names with nicknames, multiple surnames, generational suffixes, or company legal entities. It acknowledges its own limitations: it makes probabilistic guesses, not perfect identifications, and cannot verify that a parsed name is actually valid. The library includes tooling to add new training examples via the parserator command-line interface, allowing you to improve accuracy on name formats that currently stump the model.","worth_installing":"Yes, if you need to parse person or company names from unstructured text and can tolerate probabilistic (not perfect) results. The low install friction, permissive MIT license, and active maintenance make it a reasonable choice. However, the aging release cycle (654 days since last update) and Alpha development status mean you should test it on your specific name formats before relying on it in production; if accuracy is critical, verify performance on a representative sample first."},"id":"probablepeople","links":{"html":"https://skillfed.io/packages/probablepeople","md":"https://skillfed.io/packages/probablepeople.md","pypi":"https://pypi.org/project/probablepeople/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-10-29","license_spdx":null,"license_treatment":"permissive","name":"probablepeople","python_support":"supports_current","summary":"Parse romanized names & companies using advanced NLP methods"},"popularity":{"monthly_downloads":171163,"position":10377,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.6"}