Parallel Corpora & Data Annotation
Parallel Corpora for MT Systems
NLP is our DNA and we cherry pick our data engineers once they have proven an understanding of your business context. Together with native-speakers / linguists, NLPC builds Parallel Corpora for MT Systems.
OTS Parallel Corpora
Our off-the-shelf parallel corpora or specific projects are a favourite solution to companies creating machine translation systems worldwide. To create such parallel corpora for MT, we have used linguists with a high understanding of the complexities of a language structure and conversant with the syntax, and sentence structure can accurately parse and tag text according to your specifications.
Key Advantages of High-Quality Human Parallel Corpora in Machine Translation Systems
1. Enhanced Translation Accuracy: Human-generated high-quality data for building corpora in machine translation (MT) systems can significantly improve translation accuracy. Human translators have the linguistic expertise to understand nuances, cultural context, idiomatic expressions, and domain-specific terminology, resulting in more precise and contextually appropriate translations.
2. Improved Natural Language Understanding: Human high-quality data aids in improving the natural language understanding capabilities of MT systems. By incorporating human-generated data, MT models can learn to interpret and comprehend the subtleties of language, including sentiment, tone, and intent, leading to more accurate and contextually relevant translations.
3. Domain-Specific Expertise: Human-generated data allows for the inclusion of domain-specific knowledge and expertise in MT systems. When subject matter experts provide high-quality translations within their respective fields, the resulting corpora capture specialized vocabulary, technical terminology, and industry-specific nuances. This domain expertise leads to more accurate translations tailored to specific industries or professional domains.
Overall, human high-quality data for building corpora in MT systems contributes to improved translation accuracy, enhanced natural language understanding, and the inclusion of domain-specific expertise. These advantages ultimately result in more precise and contextually appropriate translations, empowering organizations to communicate effectively across languages and cultures.
Natural Language Processing Expertise for Data Annotation
From key information extraction to sentiment analysis, we can help you unlock the hidden insights contained within written text and verbal language, powering your NLP algorithms and machine learning models.
Some of our services include
Sentiment Annotation for NLP Datasets
Sentiments provide valuable insights that often drive business decisions, from purchasing and ordering to non-favorable comments for corrective action.
When you send us your data set for sentiment analysis annotation, our trained workforce annotates the sentences as positive, negative, or neutral so a machine learning model can learn from future inputs and analyze sentiments. Our proprietary text annotation tool will speed up your sentiment annotation exercise.
Types of Text Annotation
Text annotation is the process of adding additional information to a text dataset to make it more useful for machine learning and natural language processing applications. There are several different types of speech or audio annotation, including
The process of identifying and labeling the grammatical parts of speech in a sentence, such as nouns, verbs, and adjectives, etc.
The process of identifying and labeling proper nouns and other named entities in a sentence, such as people, dates, organizations, and locations.
Classifying the sentiment of a text as positive, negative, or neutral.
Identifying the main topics or themes in a piece of text.
Identifying the grammatical relationships between words in a sentence, such as subject-verb-object.
Identifying which pronouns refer to which nouns in a sentence or paragraph.
Identifying the semantic roles of words in a sentence, such as agent, patient, and instrument.
Assigning a label or category to a piece of text, such as spam or not spam, or news article or opinion piece.
Identifying the emotions expressed in a piece of text, such as anger, sadness, or happiness.
Identifying the sentiment of specific aspects or features of a product or service mentioned in text reviews, such as price, quality, or customer service.
These are just a few examples of the types of text annotation that NLPC can perform.
The specific types of annotation you require will depend on the needs and goals of your Natural Language Processing recognition system you’re developing. The quality of the text annotation has a real impact on the accuracy of the system. We have helped software companies annotating to develop anonymization / data masking tools, key information extraction and information retrieval systems.
Text annotation can be a time-consuming and labor-intensive process – but it is money well invested when the results go beyond expectations!
Why Choose Us
Why Choose NLP CONSULTANCY?
Our team is made up of Machine Learning and Deep Learning engineers, linguists, software personnel with years of experience in the development of machine translation and other NLP systems.
We don’t just sell data – we understand your business case.
Our worldwide teams have been carefully picked and have served hundreds of clients across thousands of use cases, from the from simple to the most demanding.
Proven record of successfully delivering accurate data in a secure way, on time and on budget. Our processes are designed to scale and also change with your growing needs and projects.
Do you need a regular influx of annotated data services? Are you working on a yearly budget? Our contract terms include all you need to predict ROI and succeed thanks to predictable hourly pricing designed to remove the risk of hidden costs.
Ready to get started? We are.
We’d love the opportunity to answer your questions or learn more about your project. Let us know how we can help.
