Text Segmentation (TS) is the idea of segmenting bodies of text into coherent blocks, mostly defined by the topics each segment contains. Historically, techniques in this area have been unsupervised, with more success recently coming from supervised methods instead. Although these approaches see better performance, they require training data and upfront training time. We propose a new method called Coherence, where we use strong sentence embeddings to pull representational keywords as the main constructor of sentences when comparing them to one another. Additionally, we include a storage of previously found keywords for the purposes of creating a more accurate segment representation instead of just the immediate sentence in question. With our system, we show improved results over current state-of-the-art unsupervised techniques when analyzed using Pk and WindowDiff scores. Because its unsupervised, Coherence requires no fine-tuning.

In the context of text classification, the financial burden of annotation exercises for creating training data is a critical issue. Active learning techniques, particularly those rooted in uncertainty sampling, offer a cost-effective solution by pinpointing the most instructive samples for manual annotation. Similarly, Large Language Models (LLMs) such as GPT-3.5 provide an alternative for automated annotation but come with concerns regarding their reliability. This study introduces a novel methodology that integrates human annotators and LLMs within an Active Learning framework. We conducted evaluations on three public datasets. IMDB for sentiment analysis, a Fake News dataset for authenticity discernment, and a Movie Genres dataset for multi-label classification.The proposed framework integrates human annotation with the output of LLMs, depending on the model uncertainty levels. This strategy achieves an optimal balance between cost efficiency and classification performance. The empirical results show a substantial decrease in the costs associated with data annotation while either maintaining or improving model accuracy.