Advanced Dependency Structure Analysis Using Minimum Total Penalty Method

1.句子压缩算法的发展句子压缩问题被表述为从给定句子中选择最优短语子序列的问题。然后，基于我们的依赖分析技术，提出了一个有效的算法来解决这个问题.短语间依存强度和短语重要性的估计通过使用京都大学语料库中的约34，000个句子，估计短语间依存强度。它是基于短语间依存距离的统计频率，并估计每个修改短语类和修改短语类。此外，进行了句子压缩实验，其中人类受试者压缩了200个句子。对结果进行统计分析，计算各短语类的剩余率。在此基础上，估计了每类短语的短语重要性值.压缩语句的主观评价实验 ...更多信息 针对通过使用上述算法以及估计的短语间依赖性和短语重要性来自动压缩的句子，本实验使用了200个测试句子，这些句子与2中的句子不同。采用5名被试对压缩句的质量进行评价。从以下角度进行主观评价：（a）总体印象，（B）信息保留，和c语法正确性。为了比较，同样的评价实验是由人类压缩的句子，也是通过随机的方法。实验结果表明，该方法压缩后的句子质量介于人工压缩和随机压缩之间.长句的分割由于长句很难从句法上分析，因此希望将长句分割成较短的句子。在这项工作中，支持向量机（SVM）技术被应用到这个问题。将相关短语的表面属性值组成的向量输入到支持向量机中，自动估计分割点。结果，获得了77%的准确率和84%的召回率。句子切分正确率为72%。少

1. Development of Sentence Compression AlgorithmThe sentence compression problem was formulated as a problem of selecting an optimal subsequence of phrases from a given sentence. Then, based on our dependency analysis technique, an efficient algorithm was developed to solve the problem.2. Estimation of inter-phrase dependency strength and phrase significanceBy using about 34,000 sentences in Kyoto University Corpus, inter-phrase dependency strength was estimated. It is based on the statistical frequency of inter-phrase dependency distance, and was estimated for each modifying phrase class and modified phrase class. Also, a sentence compression experiment was conducted in which human subjects compressed 200 sentences. The result was analyzed statistically and the remaining rate for each phrase class was calculated. Based on the result, phrase significance value for each phrase class was estimated.3. Subjective Evaluation of Compressed SentencesA subjective evaluation experiment was perf … More ormed for sentences automatically compressed by using the above algorithm together with the estimated inter-phrase dependency and phrase significance. In this experiment, 200 test sentences, which are different from the sentences in 2, were used. 5 subjects were employed for evaluating the quality of compressed sentences. Subjective evaluation was performed from the following points of view : (a) total impression, (b) retention of information, and c grammatical correctness. For comparison, the same kind of evaluation experiment was done for sentences compressed by humans, and also by a random method. It was found that the quality of sentences compressed by the proposed method lies just between those of human compression and random compression.4. Segmentation of Long SentencesBecause long sentences are difficult to analyze syntactically, it is desirable to segment long sentences into shorter ones. In this work, a support vector machine (SVM) technique was applied to the problem. Vectors consisting of surface attribute values of relevant phrases were input to the SVM, and segmentation points were automatically estimated. As a result, 77% of precision and 84% of recall were obtained. Correct sentence segmentation rate was 72%. Less