Text clustering, as one of the most fundamental challenges in unsupervised
learning, aims at grouping semantically similar text segments without relying
on human annotations. With the rapid development of deep learning, deep
clustering has achieved significant advantages over traditional clustering
methods. Despite the effectiveness, most existing deep text clustering methods
rely heavily on representations pre-trained in general domains, which may not
be the most suitable solution for clustering in specific target domains. To
address this issue, we propose CEIL, a novel Classification-Enhanced Iterative
Learning framework for short text clustering, which aims at generally promoting
the clustering performance by introducing a classification objective to
iteratively improve feature representations. In each iteration, we first adopt
a language model to retrieve the initial text representations, from which the
clustering results are collected using our proposed Category Disentangled
Contrastive Clustering (CDCC) algorithm. After strict data filtering and
aggregation processes, samples with clean category labels are retrieved, which
serve as supervision information to update the language model with the
classification objective via a prompt learning approach. Finally, the updated
language model with improved representation ability is used to enhance
clustering in the next iteration. Extensive experiments demonstrate that the
CEIL framework significantly improves the clustering performance over
iterations, and is generally effective on various clustering algorithms.
Moreover, by incorporating CEIL on CDCC, we achieve the state-of-the-art
clustering performance on a wide range of short text clustering benchmarks
outperforming other strong baseline methods.

提出了一种基于分类增强迭代学习架构的短文本聚类方法 CEIL，该方法使用语言模型和提出的 Category Disentangled Contrastive Clustering （CDCC）算法来迭代地改进特征表示。实验证明，CEIL 框架在各种聚类算法上显著提高了聚类性能，并超过了其他强基准方法。

一种文本聚类的通用分类增强迭代学习框架：CEIL

CEIL: A General Classification-Enhanced Iterative Learning Framework for  Text Clustering

Large pretrained language models (LMs) have shown impressive In-Context
Learning (ICL) ability, where the model learns to do an unseen task via a
prompt consisting of input-output examples as the demonstration, without any
parameter updates. The performance of ICL is highly dominated by the quality of
the selected in-context examples. However, previous selection methods are
mostly based on simple heuristics, leading to sub-optimal performance. In this
work, we formulate in-context example selection as a subset selection problem.
We propose CEIL (Compositional Exemplars for In-context Learning), which is
instantiated by Determinantal Point Processes (DPPs) to model the interaction
between the given input and in-context examples, and optimized through a
carefully-designed contrastive learning objective to obtain preference from
LMs. We validate CEIL on 12 classification and generation datasets from 7
distinct NLP tasks, including sentiment analysis, paraphrase detection, natural
language inference, commonsense reasoning, open-domain question answering, code
generation, and semantic parsing. Extensive experiments demonstrate not only
the state-of-the-art performance but also the transferability and
compositionality of CEIL, shedding new light on effective and efficient
in-context learning. Our code is released at
this https URL.

本文提出了 CEIL 算法以及其应用在 NLP 中，其将 in-context example selection 视为子集选择问题，并通过 Contrastive Learning Objective 进行优化，用于在 12 个分类和生成数据集上验证其性能。CEIL 展示了最先进的性能以及可传输性和组合性，并在 “In-Context Learning” 领域中开辟了新途径。