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Additional file 2: Table S2. Overview of sequencing and assembly of the transcriptome.

Additional file 12: Table S6. The primers used for qRT-PCR analysis.

Additional file 3: Table S3. N50 of transcripts or unigenes from nine samples.

Additional file 1: Table S1. The total anthocyanin and total flavonoid contents in the petals of three developmental stages (PF_L, PF_Z and PF_D) used for the RNA-seq analysis.

Additional file 4: Table S4. Length of transcripts or unigenes from nine samples.

The paper proposes a novel method for subtopics segmentation of Web document. An effective retrieval results may be obtained by using subtopics segmentation. The proposed method can segment hierarchically subtopics and identify the boundary of each subtopic. Based on the term frequency matrix, the method measures the similarity between adjacent blocks, such as paragraphs, passages. In the real-world sample experiment, the macro-averaged precision and recall reach 73.4% and 82.5%, and the micro-averaged precision and recal reach 72.9% and 83.1%. Moreover, this method is equally efficient to other Asian languages such as Japanese and Korean, as well as other western languages.

This paper presents a new way to extract concept that can be used to improve text classification performance (precision and recall). The computational measure will be divided into two layers. The bottom layer called document layer is concerned with extracting the concepts of particular document and the upper layer called category layer is with finding the description and subject concepts of particular category. The relevant implementation algorithm that dramatically decreases the search space is discussed in detail. The experiment based on real-world data collected from Infor-Bank shows that the approach is superior to the traditional ones.