Rich in information and annotated instances, a reference annotated dataset is essential for the training and evaluation of Natural Language Processing (NLP) tools. However, the creation of such linguistic resou
A shift from rule-based to advanced machine learning techniques, particularly transformer-based models, was observed. The Dataset sizes used in existing studies varied widely. Key challenges included the limited generalizability of proposed solutions and the need for improved integration into clinical ...
🗂️ Big Bad NLP Database ⭐ UWA Unambiguous Word Annotations - Word Sense Disambiguation Dataset ⭐ MLDoc - Corpus for Multilingual Document Classification in Eight Language [GitHub, 152 stars] Word and Sentence embeddings: ⭐ Awesome Embedding Models by Hironsan [GitHub, 1752 stars] ⭐...
(2021 ARR) e-CARE: a New Dataset for Exploring Explainable Causal Reasoning. Anonymous. [pdf] (2020 EMNLP) GLUCOSE: GeneraLized and COntextualized Story Explanations. Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll. [pdf] (2019...
2.2Statistical NLP In many cases, it is not possible to capture the complexity, subjectivity, and nuances of an entire grammar using a set of hard-coded rules. In these cases, systems based on symbolic NLP, described in Section2.1, fall short. Analyzing patterns in text using statistics and...
NLP model. As more data enters the pipeline, the model labels what it can, and the rest goes to human labelers—also known as humans in the loop, or HITL—who label the data and feed it back into the model. After several iterations, you have an accurate training dataset, ready for ...
Compared to the whole population of China it might seem like a tiny proportion, but in our dataset, it represents around 20% of the social posts. So, it’s clearly something that we should handle. This is a low estimation as some other countries and even some people in Mainland China ...
NLP models work by finding relationships between the constituent parts of language — for example, the letters, words, and sentences found in a text dataset. NLP architectures use various methods for data preprocessing, feature extraction, and modeling. Some of these processes are: ...
TaskDatasetMetricResultPre-trained Model Intent Classification SLURP Acc 86.3 link Intent Classification FSC Acc 99.6 link Intent Classification FSC Unseen Speaker Set Acc 98.6 link Intent Classification FSC Unseen Utterance Set Acc 86.4 link Intent Classification FSC Challenge Speaker Set Acc 97.5 link In...
For more, see: David Bamman, Sejal Popat and Sheng Shen, "An Annotated Dataset of Literary Entities," NAACL 2019. The entity tagging model within BookNLP is trained on an annotated dataset of 968K tokens, including the public domain materials inLitBankand a new dataset of ~500 contemporary...