使用词袋方法对古吉拉特语文本检索任务进行不同索引技术的实验
Experiments with Different Indexing Techniques for Text Retrieval tasks on Gujarati Language using Bag of Words Approach
AI总结：
本文针对形态丰富的古吉拉特语文本检索，基于词袋方法比较停用词去除、词干提取和频繁格生成等索引技术，以MAP为指标验证其相比普通词索引基线的显著改进。
摘要：
本文介绍了为提高古吉拉特语文本文档的文本检索效果而进行的各种实验结果。文本检索涉及针对给定查询词集对文本文档进行搜索和排序。我们测试了多种使用词袋方法的检索模型。词袋方法是一种沿用至今的传统方法，将文本文档表示为词的集合。频率计数、逆文档频率等度量被用来表示用户查询的相关文档并对其进行排序。使用平均精度均值这一指标，采用不同的排序模型来量化排序性能。古吉拉特语是一种形态丰富的语言，我们比较了停用词去除、词干提取和频繁格生成等技术，并与基线进行对比，以衡量信息检索任务的改进情况。大多数技术依赖于语言，需要开发特定语言的工具。我们使用未经处理的普通词索引作为基线，与基线相比，应用不同索引技术后，MAP值有了显著提高。
英文摘要：
This paper presents results of various experiments carried out to improve text retrieval of gujarati text documents. Text retrieval involves searching and ranking of text documents for a given set of query terms. We have tested various retrieval models that uses bag-of-words approach. Bag-of-words approach is a traditional approach that is being used till date where the text document is represented as collection of words. Measures like frequency count, inverse document frequency etc. are used to signify and rank relevant documents for user queries. Different ranking models have been used to quantify ranking performance using the metric of mean average precision. Gujarati is a morphologically rich language, we have compared techniques like stop word removal, stemming and frequent case generation against baseline to measure the improvements in information retrieval tasks. Most of the techniques are language dependent and requires development of language specific tools. We used plain unprocessed word index as the baseline, we have seen significant improvements in comparison of MAP values after applying different indexing techniques when compared to the baseline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
