The Chinese Uniform Meaning Representation (UMR) Annotator will support the development of high-quality semantic resources by collecting and preparing Chinese text data, performing detailed UMR annotations at both the sentence and document levels, and ensuring adherence to project annotation guidelines. The annotator will evaluate and correct preliminary UMR analyses generated by large language models (LLMs), using human expertise to improve annotation accuracy and consistency. Additional responsibilities include participating in annotation guideline refinement, quality assurance, and inter-annotator agreement exercises, as well as documenting challenging annotation cases. Candidates should have native or near-native proficiency in Chinese, strong analytical and linguistic skills, and an interest in computational linguistics, natural language processing, or semantic annotation.
The hourly rate for the position is $21.76
Function 1: Perform Chinese UMR annotation
Function 2: Data collection for UMR annotation
Function 3: Evaluating the output of pre-processing Tools such as LLMs
Function 4: Guideline refinement, quality assurance
Job Requirements:
Bachelor's degree preferred.
Candidates should have native or near-native proficiency in Chinese,
Strong analytical and linguistic skills, and an interest in computational linguistics, natural language processing, or semantic annotation.
The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements.
Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class").
