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[Перевод] Распознавание именованных сущностей: механизм, методики, сценарии использования и реализация

Естественные языки сложны. А когда на горизонте появляется контекст, они становятся ещё сложнее. Возьмём для примера фамилию Линкольн. Некоторые сразу подумают о шестнадцатом президенте США, выдающейся исторической фигуре. Однако для других это производитель автомобилей с тем же названием. Одно простое слово имеет разные значения.

Мы, люди, без проблем различаем значения и категории. Это свидетельствует о нашем интуитивном понимании окружающего мира. Но когда дело касается компьютеров, эта, казалось бы, простая задача превращается в неоднозначную проблему. Подобные трудности подчёркивают необходимость надёжного распознавания именованных сущностей (named entity recognition, NER) — механизма, при помощи которого мы учим машины понимать различные лингвистические нюансы.

В этой статье мы расскажем о том, что такое NER, о его принципах работы и о том, как оно используется в реальной жизни. Также в ней мы прольём свет на различные методики NER и способы реализации модели NER.

Что такое Named Entity Recognition (NER)?

Named entity recognition (NER) — это область обработки естественных языков (natural language processing, NLP), занимающаяся идентификацией и классификацией специфических примеров данных из текстового содержимого. NER работает с характерными деталями текста — именованными сущностями : отдельными словами, фразами или последовательностями слов, идентифицируя и категоризируя их в заранее выбранные группы. Эти категории охватывают разнообразный диапазон представленных в тексте объектов, в том числе имена людей, географические объекты, названия организаций, даты, событий и даже отдельные количественные значения, например, деньги и проценты.

Ключевые концепции NER

Именованные сущности — не единственная концепция, которую нужно понять в мире NER. Чтобы лучше разобрать тему, необходимо раскрыть множество других терминов.

Разметка частей речи (POS tagging). Это процесс присвоения словам в тексте меток, соответствующих их части речи, например, «прилагательное», «глагол» или «существительное».

Корпус. Это коллекция текстов, используемая для лингвистического анализа и обучения моделей NER. Корпусом может быть и набор новостных статей, и научные журналы, и даже посты в соцсетях.

Группирование (chunking). Это методика NLP, группирующая отдельные слова или фразы в «блоки» (chunk) на основании их синтаксических ролей, создавая осмысленные кластеры, например, глагольные и именные конструкции.

Эмбеддинги слов (word embeddings). Это описания слов в виде плотных векторов, передающие их синтаксические значения. Эмбеддинги слов преобразуют слова или фразы в числовые векторы фиксированного размера, что упрощает их обработку моделями машинного обучения. Популярными инструментами для генерации таких эмбеддингов являются Word2Vec и GloVe, они помогают понимать контекст и взаимосвязи слов в тексте.

Пример NER

Рассмотрим предложение: «Mary from the HR department said that The Ritz London was a great hotel option to stay in London».

NER, выполненное для предложения при помощи displaCy Named Entity Visualizer.

В этом предложении:

«Mary» помечена как PERSON (человек), показывая, что это сущность, обозначающая имя человека.
«The Ritz» помечен как ORG, что обозначает Organization (организация). Это значит, что этот фрагмент распознан как сущность, относящаяся к компаниям, агентствам, учреждениям и так далее.
«London» классифицирован как GPE, что расшифровывается как Geopolitical entity (геополитическая сущность). GPE описывают страны, города, штаты и любые другие регионы с определёнными границами и управлением.

По сути, если ваша цель — ответить на вопросы кто, где, что и когда по тексту, то нужно использовать методику NER.

NER для структурирования неструктурированных данных

NER играет важнейшую роль в преобразовании неструктурированного текста в структурированные данные. Оно систематически идентифицирует и категоризирует такие ключевые элементы, как имена, места, даты и другие термины внутри текста. Благодаря этому NER преобразует огромные объёмы текстового содержимого в упорядоченные датасеты, подготовленные к дальнейшему анализу. Например, NER способно быстро извлекать названия компаний, курсы акций и экономические метрики из новостной статьи, превращая пространные тексты в сжатые сводки.

Основные сценарии использования NER

Распознавание именованных сущностей влечёт за собой прогресс в различных отраслях, позволяя совершенствовать процессы и делать более точные выводы. Ниже мы перечислим одни из самых важных областей его применения.

Извлечение информации

Извлечение информации (information retrieval) — это процесс получения информации (часто из крупных датасетов), которая релевантна определённому запросу или потребности.

Для примера рассмотрим сферу поисковых движков, где NER используется для повышения точности результатов поиска. Например, если исследователь вводит в базу данных или поисковый движок «Инновации батарей Tesla в 2023», то NER определяет, что «Tesla» — это компания, а «2023» — это конкретный год. Это гарантирует, что полученные результаты будут напрямую связаны с прогрессом Tesla в разработке батарей в 2023 году и не будут включать в себя несвязанные с этим статьи о Tesla или общих инновациях в разработке аккумуляторов.

Автоматизированный ввод данных

Роботизированная автоматизация процессов (Robotic Process Automation, RPA) — это система компьютерных программ, воссоздающих человеческие действия для выполнения повседневных бизнес-задач. Хотя эти программы не связаны с аппаратными роботами, они работают подобно обычным «белым воротничкам».

В корпоративной экосистеме автоматизированный ввод данных, дополненный NER, совершил революцию в рабочих процессах. Возьмём для примера логистическую компанию, ежедневно работающую с тысячами накладных. Система RPA, оснащённая NER, может сканировать эти документы, выявляя такие сущности, как «отправитель», «грузополучатель» и «наименование перевозчика». После их распознавания система может извлекать и вводить эти данных в управляющее ПО компании для упрощения доступа и упорядочивания.

Улучшение качества анализа эмоционального настроя

Анализ эмоционального настроя (sentiment analysis) — это методика, позволяющая при помощи статистики, NLP и машинного обучения распознавать и извлекать из текста субъективный контекст. К нему могут относиться эмоции, мнения и оценки автора относительно какой-то темы, события или действий компании.

В сочетании с NER анализ эмоционального настроя обеспечивает компаниям детальное понимание отзывов клиентов. И этот процесс может быть очень глубоким. Рассмотрим следующий пример отзыва на отель: «Номер просторный, с потрясающим видом из окна, но выбор на шведском столе был довольно ограниченным». При помощи NER инструменты анализа эмоционального настроя способны определить, что положительная оценка относится к «номеру» и «виду из окна», а негативная — к «шведскому столу». Подобные детальные наблюдения позволяют отелям совершенствовать конкретные аспекты и оценивать впечатления постояльцев.

Как работает NER

Принципиально NER основано на двух основных этапах:

идентификации сущностей в тексте и
категоризации этих сущностей на уникальные группы.

Два этапа процесса NER, выполняемые с фрагментом из книги «В дороге» Джека Керуака.

Давайте рассмотрим каждый из этапов подробнее.

Распознавание сущностей

Распознавание сущностей, часто называемое распознаванием упоминаний (mention detection) или идентификацией именованных сущностей (named entity identification) — это первоначальный и фундаментальный этап процесса NER. Он заключается в систематическом сканировании и выявлении блоков текста, которые потенциально могут представлять значимые сущности.

Токенизация. По своей сути, документ или предложение — это всего лишь длинная строка символов. Токенизация — это процесс разбиения этой строки на значимые фрагменты, называемые токенами. В английском языке токены часто эквивалентны словам, но могут означать также знаки препинания и другие символы. Такая сегментация упрощает последующие аналитические этапы благодаря преобразованию текста в удобные для работы единицы.

Извлечение признаков. Простого разбиения текста недостаточно. Следующая задача заключается в понимании значения этих токенов. Именно тут вступает в дело извлечение признаков (feature extraction).

Оно заключается в анализе свойств токенов, например:

морфологических признаков, касающихся формы слов, например, их начальных форм или префиксов;
синтаксических признаков, выделяющих структуру и взаимосвязь слов в предложениях;
семантических признаков, передающих внутренний смысл слов и иногда для понимания роли токена касающихся более широких знаний о мире или контекста.

Последующим этапом процесса NER является классификация сущностей.

Классификация сущностей

Классификация сущностей заключается в распределении распознанных сущностей по отдельным категориям или классам на основании их семантического значения и контекста. Примерами таких категорий могут быть человек, организация, место, дата и множество других меток, зависящих от требований области применения.

Классификация сущностей — это процесс со множеством нюансов, ему нужно точное понимание контекста, в котором встречаются сущности. В этой классификации используются лингвистические, статистические, а иногда и предметные знания. Например, «Apple» в сфере технологий может относиться к технологической компании, а в кулинарном контексте — к фрукту.

Ещё один пример: «Summer played amazing basketball»; здесь слово «Summer» будет классифицировано как человек из-за контекстуальной подсказки, создаваемой словом «basketball». Однако без наличия таких подсказок «Summer» также может означать время года. Такие неоднозначности в естественном языке часто требуют лингвистического анализа или сложных моделей NER, на обширных датасетах обученных различать возможные значения.

Способы реализации NER

За годы своего существования NER существенно эволюционировало, для решения его задач было выработано множество подходов. Ниже мы расскажем о распространённых методиках NER.

Методика NER, основанная на правилах

При идентификации и классификации именованных сущностей основанные на правилах методики NER применяют наборы заранее определённых правил или паттернов.

Эти правила часто выводятся из лингвистических наблюдений и кодифицируются в систему при помощи следующих техник:

Регулярные выражения. Это сопоставление паттернов для выявления сущностей на основании известных структур, например, телефонных номеров или адресов электронной почты.

Поиск в словарях. Использование заранее заданных списков или баз данных именованных сущностей для поиска совпадений в тексте. Допустим, у системы есть словарь, содержащий имена известных авторов, например, «Джейн Остин», «Эрнест Хемингуэй» и «Джордж Оруэлл». Когда она встречает предложение «Недавно я прочитал рассказ Джорджа Оруэлла», то благодаря списку поиска быстро распознаёт в «Джордж Оруэлл» именованную сущность, относящуюся к автору.

Правила на основе паттернов. Использование для извлечения сущностей определённых лингвистических структур. Слово с заглавной буквы посередине предложения может свидетельствовать об имени собственном. Например, в предложении «Если вы когда-нибудь поедете в Лондон, то обязательно посетите Британский музей, чтобы погрузиться в мир искусства и истории» слово «Лондон» с заглавной буквы намекает, что это имя собственное, являющееся местом.

Зачем использовать эту методику? Методика на основе правил лучше всего работает, когда дело касается конкретных предметных областей, где сущности соответствуют чётким и постоянным паттернам. Такие методики могут быть эффективны и просты, если вы точно знаете, что ищете и если паттерны меняются нечасто.

Методика NER на основе машинного обучения

В мире традиционных методик машинного обучения для NER модели обучаются на данных, в которых сущности размечены. Например, в предложении «Париж — столица Франции» слова «Париж» и «Франции» могут быть размечены как GPE.

В такой методике активно применяется инжиниринг признаков, при котором для повышения точности модели конкретные атрибуты и информация о данных извлекаются вручную.

Примеры распространённых признаков:

характеристики слов — такие подробности, как регистр слов (с заглавной буквы, в нижнем или в верхнем регистре), префиксы и суффиксы;
контекст — окружающие слова, как предыдущие, так и последующие, дающие дополнительную информацию;
синтаксическая информация — метки частей речи, проливающие свет на функцию слова в предложении, например, существительное, глагол, прилагательное и так далее;
паттерны слов — форма паттерна слова, особенно при распознавании конкретных форматов, например, дат или регистрационных номеров автомобилей;
морфологические подробности — информация, извлекаемая из начальной формы слова, или его морфологические нюансы.

После подготовки признаков модель обучается на этих дополненных данных. При этом используются такие алгоритмы, как Support Vector Machines (SVM), Decision Trees и Conditional Random Fields (CRF). После обучения модель может прогнозировать или размечать сущности в сырых, неразмеченных данных.

Зачем использовать эту методику? Методику на основе машинного обучения следует применять, когда сущности имеют разные типы и не всегда следуют постоянному паттерну. Если у вас есть доступ к аннотированным данным, то вы сможете обучить модель, способную хорошо обобщать выводы на разнообразных паттернах сущностей.

Несмотря на свою эффективность, такая методика требует существенных усилий на этапе инжиниринга признаков, в отличие от методики на основе правил, в которых они вообще не требуются, или методик глубокого обучения, которые автоматически могут извлекать важные признаки из данных.

Методика NER на основе глубокого обучения

Глубокое обучение — это более автоматизированный и сложный способ реализации NER. Глубокое обучение — это подраздел машинного обучения, в котором искусственные нейросети (сложные алгоритмы, работающие подобно человеческому мозгу) учатся на огромных датасетах. По сути, глубокое обучение нацелено на обучение компьютеров воспроизводить то, как люди учатся и обрабатывают информацию.

Как сказано выше, существенное преимущество глубокого обучения перед традиционным машинным обучением — возможность автоматического изучения признаков в данных. Эта способность позволяет снизить зависимость от трудозатратного ручного инжиниринга признаков, применяемого в традиционных методиках.

Для подготовки моделей глубокого обучения, как и в традиционных методиках, необходимы аннотированные данные. Однако благодаря возможностям таких моделей по обработке огромных датасетов и их сложной структуре они часто обгоняют по точности своих конкурентов, особенно при наличии достаточного объёма обучающих данных.

В сфере NER популярны различные архитектуры сетей.

Рекурентные нейросети (Recurrent Neural Networks, RNN) и сети с долгой кратковременной памятью (Long Short-Term Memory, LSTM) усваивают последовательную информацию, делая её подходящей для обработки текстовых данных с контекстом.
Архитектуры трансформеров, в том числе GPT и подобные ему, изменили ситуацию в мире NLP, в том числе и NER. Их способность одновременно уделять внимание различным частям входных данных позволяет глубже понимать контекст.

Что касается GPT и распознавания сущностей, хотя модели наподобие ChatGPT успешно справляются со многими задачами, они являются частью широкого спектра инструментов для NER. Однако для задач чистого распознавания сущностей в некоторых случаях могут быть предпочтительны другие специализированные модели.

Зачем использовать эту методику? Преимущества глубокого обучения проявляются в задачах, требующих высокого уровня сложности при условии наличия огромного объёма данных. В отличие от традиционного машинного обучения, оно способно автоматически выявлять в данных релевантные признаки и обучаться им, благодаря чему оно особенно эффективно в контекстах с высокой неоднозначностью или разнообразием, где сущности могут быть многогранными.

Как реализовать NER

Для NER существует огромный спектр инструментов и библиотек. Однако необходимо знать о пересекающихся стратегиях, усиливающих их потенциал: трансферном и активном обучении. Трансферное обучение заключается в адаптации предварительно обученной модели наподобие GPT-4 или RoBERTa под конкретную задачу NER. Применение предварительно обученных архитектур позволяет экономить на вычислительных затратах и часто приводит к повышению точности. В свою очередь, активное обучение итеративно повторно обучает модель на сложных примерах, повышая её эффективность и скорость сходимости.

Помня об этих стратегиях, давайте углубимся в изучение инструментов и библиотек, помогающих в реализации NER.

Инструменты и библиотеки для создания модели NER

В этом разделе мы вкратце рассмотрим несколько примеров инструментов и библиотек, подходящих для задач NER. Это лишь малая часть из доступных инструментов, поэтому если вы решили создать собственную модель NER, то мы рекомендуем провести собственные исследования.

spaCy — это бесплатная опенсорсная библиотека на Python для задач NLP. У неё есть такие функции, как NER, разметка частей речи, парсинг зависимостей и векторы слов. EntityRecognizer — это компонент spaCy для распознавания именованных сущностей с чётким и определённым их упоминанием. Однако его архитектура может быть неоптимальной для задач, в которых определения сущностей неоднозначны или когда ключевая информация находится посередине сущностей.

NLTK (Natural Language Toolkit) — это платформа для создания программ на Python, работающих с данными естественных языков. Хотя эта платформа в основном известна своими возможностями в анализе лингвистических данных, её также можно использовать для NER.

Stanford NLP имеет большой спектр инструментов для обработки естественного языка. Одна из его выдающихся функций — это RegexNER, интерфейс на основе правил, специально разработанный для NER при помощи регулярных выражений. Хотя ядро Stanford NLP написано на Java, в нём есть обёртка для Python, что позволяет использовать его возможности разработчикам на Python.

NeuroNER — это программа, разработанная специально для распознавания именованных сущностей при помощи нейросетей.

Как работает NeuroNER

NeuroNER позволяет создавать или изменять аннотации в новом или имеющемся корпусе, обеспечивая адаптированные и точные результаты распознавания сущностей.

DeepPavlov — это опенсорсная библиотека для разговорного AI, основанного на библиотеках ML наподобие TensorFlow и Keras; в ней есть коллекция предварительно обученных моделей NER, подходящих для глубокого обучения.

BRAT (Brat Rapid Annotation Tool) — это веб-решение, позволяющее аннотировать текст, размечать сущности и их сложные взаимосвязи.

API для NER

Ещё один аспект, который должны учитывать разработчики — это применение интерфейсов программирования приложений (API). Они предоставляют удобный способ использования возможностей NER без необходимости погружения в дебри обучения моделей и бэкендов.

Google Cloud NLP — это облачный продукт Google для задач обработки естественного языка, включающий в себя мощную систему распознавания именованных сущностей и классификации сущностей в тексте.

Демо NLP API

Сервис обработки естественного языка Amazon AWS Comprehend для исследования текста, выявления сущностей и многого другого использует машинное обучение. Согласно официальным ресурсам, Amazon Comprehend использует NLP для извлечения важных выводов о содержимом документов. Он определяет сущности, ключевые фразы, язык, эмоциональный настрой и другие превалирующие элементы документов.

IBM Watson NLU — это компонент большого набора программ IBM AI с функциями распознавания сущностей. Watson Natural Language Understanding, исследующий неструктурированные текстовые данные, применяет глубокое обучение для расшифровки смысла и извлечения метаданных. Благодаря аналитике текста он может выявлять категории, классификации, сущности, ключевые слова, эмоциональный настрой, чувства, взаимосвязи, что позволяет пользователям глубже понимать и анализировать их данные.

OpenAI GPT-4 API предлагает разработчикам доступ к одной из самых совершенных языковых моделей. Хотя его основная сильная сторона заключается в генерации текста, напоминающего написанный человеком, при помощи структурированных определённым образом запросов его можно использовать и для таких задач, как распознавание именованных сущностей. Это особенно полезно, когда требования заключаются не только в выявлении сущностей, но и в понимании контекста, извлечении выводов или в ответе на связанные с текстом вопросы. Гибкость модели GPT-4 делает её удобным кандидатом на выполнение различных задач NLP, включая и NER.

Как видите, существуют различные продукты, способные выполнять за вас задачи NER. При выборе между созданием собственной модели NER или использованием API необходимо учитывать свои специфические потребности и вопрос уязвимости данных. Собственные модели обеспечивают гибкость при выполнении нишевых задач и гарантируют приватность данных, но требуют больше ресурсов. API быстры и экономичны при выполнении общих задач, но могут не отвечать специализированным требованиям.

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22 мая 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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06.09.24 01:44 Celinagarcia

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23.09.24 18:56 matthewshimself

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

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26.11.24 21:59 [email protected]

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26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.