Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this study,  have proposed a domain-independent unsupervised text segmentation method, which is applicable to even if unseen single document. This proposed method segments text documents by evaluating similarity beten sentences. It is generally difficult to calculate semantic similarity beten words that comprise sentences when the domain knowledge is insufficient. This problem influences segmentation accuracy. To address this problem,  use word 2 vec to calculate semantic similarity beten words. Using word 2 vec,  embed semantic relationships beten words in a vector space by training with large domain-independent corpora. Furthermore,  combine semantic and collocation similarities, i.e., The features beten words within a document. The proposed method applies this combined similarity to affinity propagation clustering. Similarity beten sentences is defined based on the earth mover's distance beten the frequencies of the obtained topical clusters. After calculating similarity beten sentences, segmentation boundaries are automatically optimized using dynamic programming. The experimental results obtained using two datasets show that the proposed method clearly outperforms state-of-the-art domain-independent approaches and obtains equal performance with state-of-the-art domain-dependent approaches such as those that use topic modeling.
['Makoto Sakahara', 'Shogo Okada', 'Katsumi Nitta']
I am writing a research paper on a idea Domain-Independent Unsupervised Text Segmentation for Data Management
[{"from": "human", "value": "I am writing a research paper on a idea Domain-Independent Unsupervised Text Segmentation for Data Management"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this study,  have proposed a domain-independent unsupervised text segmentation method, which is applicable to even if unseen single document. This proposed method segments text documents by evaluating similarity beten sentences. It is generally difficult to calculate semantic similarity beten words that comprise sentences when the domain knowledge is insufficient. This problem influences segmentation accuracy. To address this problem,  use word 2 vec to calculate semantic similarity beten words. Using word 2 vec,  embed semantic relationships beten words in a vector space by training with large domain-independent corpora. Furthermore,  combine semantic and collocation similarities, i.e., The features beten words within a document. The proposed method applies this combined similarity to affinity propagation clustering. Similarity beten sentences is defined based on the earth mover's distance beten the frequencies of the obtained topical clusters. After calculating similarity beten sentences, segmentation boundaries are automatically optimized using dynamic programming. The experimental results obtained using two datasets show that the proposed method clearly outperforms state-of-the-art domain-independent approaches and obtains equal performance with state-of-the-art domain-dependent approaches such as those that use topic modeling."}]