  While supervised learning models have shown remarkable performance in various
natural language processing (NLP) tasks, their success heavily relies on the
availability of large-scale labeled datasets, which can be costly and
time-consuming to obtain. Conversely, unsupervised learning techniques can
leverage abundant unlabeled text data to learn rich representations, but they
do not directly optimize for specific NLP tasks. This paper presents a novel
hybrid approach that synergizes unsupervised and supervised learning to improve
the accuracy of NLP task modeling. While supervised models excel at specific
tasks, they rely on large labeled datasets. Unsupervised techniques can learn
rich representations from abundant unlabeled text but don't directly optimize
for tasks. Our methodology integrates an unsupervised module that learns
representations from unlabeled corpora (e.g., language models, word embeddings)
and a supervised module that leverages these representations to enhance
task-specific models. We evaluate our approach on text classification and named
entity recognition (NER), demonstrating consistent performance gains over
supervised baselines. For text classification, contextual word embeddings from
a language model pretrain a recurrent or transformer-based classifier. For NER,
word embeddings initialize a BiLSTM sequence labeler. By synergizing
techniques, our hybrid approach achieves SOTA results on benchmark datasets,
paving the way for more data-efficient and robust NLP systems.
