RICH SHORT TEXT CONVERSION USING SEMANTIC KEY CONTROLLED SEQUENCE GENERATION

Abstract

Short text data such as search queries, social media posts, and user inputs often lack sufficient context and semantic richness, making it difficult for traditional natural language processing (NLP) systems to generate meaningful outputs. These limitations affect applications such as chatbots, recommendation systems, and content generation. This project proposes a novel approach for transforming short text into rich, context-aware content using semantic key-controlled sequence generation, leveraging advanced deep learning techniques. The proposed system extracts key semantic features from short input text using embedding techniques such as Word2Vec, GloVe, or transformer-based models. These semantic keys act as control signals that guide the sequence generation process. A deep learning architecture, such as a Sequence-toSequence (Seq2Seq) model with attention mechanism or transformer-based models like GPT, is employed to generate expanded and meaningful text. The model learns contextual relationships and generates coherent sentences by conditioning on the extracted semantic keys, ensuring that the output remains relevant to the input. The methodology includes preprocessing steps such as tokenization, stop-word removal, and embedding generation. The model is trained on large text corpora to learn language patterns and semantic relationships. During inference, the system takes short input text, extracts semantic keys, and generates enriched output sequences. Experimental results demonstrate that the proposed approach significantly improves text quality, coherence, and contextual relevance compared to traditional text generation methods. However, challenges such as controlling output diversity and computational complexity remain.