Text Stream

Text stream processing focuses on efficiently analyzing and extracting information from continuously arriving sequences of text data, a challenge posed by the ever-increasing volume of online textual information. Current research emphasizes developing models capable of handling the infinite length, sparsity, and evolving nature of these streams, often employing techniques like linear-time decoding, sliding window approaches, and incremental learning algorithms such as those based on SBERT and incremental SVMs. This field is crucial for real-time applications like zero-shot text-to-speech, LLM-based text streaming services, and sentiment analysis, improving user experience and enabling timely insights from dynamic textual data.