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Dive into our Samples of NLP Assignments! Here, you can get a sneak peek into the quality and expertise we offer in Natural Language Processing (NLP) assignment assistance. Browse through our curated selection of sample assignments to see firsthand the level of detail, accuracy, and clarity we bring to every project.
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| Service provided by our NLP Experts | Price (USD) | Time (Hours) |
|---|---|---|
| Sentiment Analysis | $20 | 24 |
| Named Entity Recognition (NER) | $30 | 48 |
| Text Summarization | $40 | 72 |
| Machine Translation | $50 | 96 |
| Part-of-Speech Tagging (POS) | $60 | 120 |
| Topic Modeling | $70 | 144 |
| Speech Recognition | $80 | 168 |
- Natural Language Processing Assignment Help
- Extracting Entities from Text in Natural Language Processing
- How Natural Language Processing Assignment Help Benefits Students?
- Extensive Coverage of NLP Topics In our Natural Language Processing Assignment Help
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Natural Language Processing Assignment Help
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Extracting Entities from Text in Natural Language Processing
Python Code
import spacy
# Load the English language model
nlp = spacy.load("en_core_web_sm")
# Sample text for Named Entity Recognition
text = "Apple Inc. is headquartered in Cupertino, California. It was founded by Steve Jobs."
# Process the text using spaCy
doc = nlp(text)
# Extract named entities
entities = [(entity.text, entity.label_) for entity in doc.ents]
# Print named entities and their labels
for entity, label in entities:
print(f"Entity: {entity}, Label: {label}")
This code demonstrates how spaCy can be used for Named Entity Recognition, identifying and categorizing named entities like organizations (e.g., Apple Inc.), locations (e.g., Cupertino, California), and people (e.g., Steve Jobs) in a given text.
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- Named Entity Recognition (NER): Our experts help to solve assignments with Identifying and classifying named entities in text, such as people, organizations, locations, dates, and more, which is useful for information extraction tasks.
- Text Classification: Experts Solves Categorizing text documents into predefined classes or categories based on their content, commonly used in spam filtering, topic categorization, and sentiment classification.
- Topic Modeling: Assignment is done by experts extracting topics from a collection of text documents to uncover hidden patterns and themes, facilitating tasks like document clustering and content recommendation.
- Language Generation: We provide helpers to generate human-like text based on a given prompt or computational linguistics, which is employed in chatbots in search engines, automated content generation, and machine translation systems.
- Text Summarization: Our assistance condensing the content of a text document while preserving its key information and meaning, useful for creating concise summaries of large texts.
- Part-of-Speech Tagging: We help in assigning grammatical tags to words in a sentence to identify their syntactic roles, aiding in tasks such as text analysis, parsing, and machine translation.
- Machine Translation: Our team is adept at translating text from one language to another automatically using computational methods, enabling cross-language communication and content localization.
- Question Answering: We help in generating answers to questions posed in natural language, often utilizing large knowledge bases or pre-trained models.
- Word Embeddings: We develop words as dense vectors in a high-dimensional space, capturing semantic relationships between words and enabling better performance in NLP tasks like language modeling and document classification.
- Language Understanding: Our team helps to make the task and understand the meaning and intent behind natural language input, including tasks like intent classification, entity recognition, and semantic parsing.
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PhD in Computer Science
🇺🇸 United States
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