Experts across diverse disciplines are often interested in making sense of
large text collections. Traditionally, this challenge is approached either by
noisy unsupervised techniques such as topic models, or by following a manual
theme discovery process. In this paper, we expand the definition of a theme to
account for more than just a word distribution, and include generalized
concepts deemed relevant by domain experts. Then, we propose an interactive
framework that receives and encodes expert feedback at different levels of
abstraction. Our framework strikes a balance between automation and manual
coding, allowing experts to maintain control of their study while reducing the
manual effort required.

本研究提出了一种交互式框架，该框架通过接收并编码领域相关的概念和专家反馈来维护领域专家对学习的控制，从而在自动化和手动编码之间取得平衡。

大型文本数据中的交互式概念学习和主题发现

Interactive Concept Learning for Uncovering Latent Themes in Large Text  Collections

We present Spacerini, a modular framework for seamless building and
deployment of interactive search applications, designed to facilitate the
qualitative analysis of large scale research datasets. Spacerini integrates
features from both the Pyserini toolkit and the Hugging Face ecosystem to ease
the indexing text collections and deploy them as search engines for ad-hoc
exploration and to make the retrieval of relevant data points quick and
efficient. The user-friendly interface enables searching through massive
datasets in a no-code fashion, making Spacerini broadly accessible to anyone
looking to qualitatively audit their text collections. This is useful both to
IR~researchers aiming to demonstrate the capabilities of their indexes in a
simple and interactive way, and to NLP~researchers looking to better understand
and audit the failure modes of large language models. The framework is open
source and available on GitHub: this https URL, and
includes utilities to load, pre-process, index, and deploy local and web search
applications. A portfolio of applications created with Spacerini for a
multitude of use cases can be found by visiting this https URL.

Spacerini 旨在为大规模研究数据集的定性分析提供便利，合并了 Pyserini 工具包和 Hugging Face 生态系统的功能，使得通过易于使用的用户界面以非编码方式搜索大量数据集变得广泛可用。

使用 Pyserini 和 Hugging Face 打造即插即用的搜索引擎 Spacerini

Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face

This chapter argues for more informed target metrics for the statistical
processing of stylistic variation in text collections. Much as operationalised
relevance proved a useful goal to strive for in information retrieval, research
in textual stylistics, whether application oriented or philologically inclined,
needs goals formulated in terms of pertinence, relevance, and utility - notions
that agree with reader experience of text. Differences readers are aware of are
mostly based on utility - not on textual characteristics per se. Mostly,
readers report stylistic differences in terms of genres. Genres, while vague
and undefined, are well-established and talked about: very early on, readers
learn to distinguish genres. This chapter discusses variation given by genre,
and contrasts it to variation occasioned by individual choice.

本章提出了更明智的文本风格变异统计目标，主要分别从文本学和应用角度阐述了在受众阅读经验的基础上制定的目标所必须具备的关键概念：相关性、有效性和实用性，并探讨了文本风格的变异因素是个体选择而非纯文本特征本身，而读者对特定文本的评价通常基于文体类型的分类。