Text Segmentation based on Semantic Word Embeddings
Table 6: Effects of text representation on the performance of the C99 algorithm. The cut column denotes the cutoff for the length of a sentence after preprocessing. The stop column denotes whether stop words and punctuation are removed. The stem column denotes whether the words are passed through the Porter stemming algorithm. The rank column denotes the size of the kernel for the ranking transformation. Evaluations are given both as the Pk metric and the Window Diff (WD) score. All experiments are done on the 400 test documents in the 3–11 set of the Choi dataset. The upper section cites results contained in the CWM 2000 paper [4]. The second section is an attempt to match these results with our implementation (oC99). The third section attempts to give an overview of the effect of different parameter choices for the representation step of the algorithm. The last section reports our best observed result as well as a run (TBR) with the same parameter settings, but with a tie-breaking strategy that takes right-most rather then left-most equal value.
['note', 'cut', 'stop', 'stem', 'rank', '[ITALIC] Pk (%)', 'WD (%)']
We implemented our own version of the C99 algorithm (oC99) and tested it on the Choi dataset. We explored the effect of various changes to the representation part of the algorithm, namely the effects of removing stop words, cutting small sentence sizes, stemming the words, and performing the rank transformation on the cosine similarity matrix. For stemming, the implementation of the Porter stemming algorithm from nltk was used. For stopwords, we used the list distributed with the C99 code augmented by a list of punctuation marks. This is likely due to particulars relating to details of the text transformations, such at the precise stemming algorithm and the stopword list.