Natural Language Processing (NLP) Benchmark Corpus
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An Natural Language Processing (NLP) Benchmark Corpus is an NLP corpus that supports an NLP benchmark task to evaluate NLP system performance.
- It can (typically) be a curated collection of data.
- It can include NLP Benchmark Task Measure.
- It can (often) include Ground Truth Annotated Data.
- It can (often) contain a diverse set of texts, conversations, or linguistic examples relevant to specific NLP tasks.
- It can range from being ... text document-based corpuss, transcribed speeches, social media posts, and dialogue exchanges.
- It can (often )reflect a wide range of linguistic phenomena and challenges.
- It can range from being a Unilingual NLP Benchmark Corpus to being a Multilingual NLP Benchmark Corpus.
- A Sentiment Analysis Benchmark Corpus, such as the dataset used for evaluating sentiment detection algorithms.
- A Language Modeling Benchmark Corpus, like those used in training and testing statistical language models.
- A Machine Translation Benchmark Corpus, such as parallel text corpora used for evaluating translation algorithms.
- A Question-Answering Benchmark Corpus, like the SQuAD Dataset, used for machine comprehension tests.
- The GLUE Benchmark corpus, which evaluates model performance across multiple NLP tasks.
- TREC Text REtrieval Conference datasets, used in information retrieval and search tasks.
- A Chatbot Evaluation Benchmark Query/Responses Dataset, used for assessing chatbot interactions.
- A Multilingual NLP Benchmark Corpus for cross-language model evaluation.
- A randomly collected set of texts not specifically curated for benchmarking.
- A training dataset used for machine learning models, which may not be designed for evaluation.
- A corpus focusing only on a specific domain, lacking the diversity required for a benchmark.
- See: ML Benchmark Dataset, BIG-Bench Benchmark, Language Model, Text Analytics.
- (Srivastava et al., 2022) ⇒ Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, and others. (2022). “Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models.” In: arXiv preprint arXiv:2206.04615.