Hierarchical text classification aims to categorize each document into a set
of classes in a label taxonomy. Most earlier works focus on fully or
semi-supervised methods that require a large amount of human annotated data
which is costly and time-consuming to acquire. To alleviate human efforts, in
this paper, we work on hierarchical text classification with the minimal amount
of supervision: using the sole class name of each node as the only supervision.
Recently, large language models (LLM) show competitive performance on various
tasks through zero-shot prompting, but this method performs poorly in the
hierarchical setting, because it is ineffective to include the large and
structured label space in a prompt. On the other hand, previous
weakly-supervised hierarchical text classification methods only utilize the raw
taxonomy skeleton and ignore the rich information hidden in the text corpus
that can serve as additional class-indicative features. To tackle the above
challenges, we propose TELEClass, Taxonomy Enrichment and LLM-Enhanced
weakly-supervised hierarchical text classification, which (1) automatically
enriches the label taxonomy with class-indicative topical terms mined from the
corpus to facilitate classifier training and (2) utilizes LLMs for both data
annotation and creation tailored for the hierarchical label space. Experiments
show that TELEClass can outperform previous weakly-supervised hierarchical text
classification methods and LLM-based zero-shot prompting methods on two public
datasets.

通过自动丰富标签分类法和利用大型语言模型来进行弱监督的分层文本分类，我们的方法 TELEClass 在两个公共数据集上表现优于先前的弱监督分层文本分类方法和基于零样本提示的大型语言模型方法。

TELEClass: 在最小监督下的分类法丰富和 LLM 增强的分层文本分类

TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text  Classification with Minimal Supervision

Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a
taxonomic backbone that allows the arrangement and structuring of various
concepts in accordance with the hypo-hypernym ("class-subclass") relationship.
With the rapid growth of lexical resources for specific domains, the problem of
automatic extension of the existing knowledge bases with new words is becoming
more and more widespread. In this paper, we address the problem of taxonomy
enrichment which aims at adding new words to the existing taxonomy.
We present a new method that allows achieving high results on this task with
little effort. It uses the resources which exist for the majority of languages,
making the method universal. We extend our method by incorporating deep
representations of graph structures like node2vec, Poincaré embeddings, GCN
etc. that have recently demonstrated promising results on various NLP tasks.
Furthermore, combining these representations with word embeddings allows us to
beat the state of the art.
We conduct a comprehensive study of the existing approaches to taxonomy
enrichment based on word and graph vector representations and their fusion
approaches. We also explore the ways of using deep learning architectures to
extend the taxonomic backbones of knowledge graphs. We create a number of
datasets for taxonomy extension for English and Russian. We achieve
state-of-the-art results across different datasets and provide an in-depth
error analysis of mistakes.

本文研究领域为知识图谱，提出了一种基于图结构表示与深度学习的分类体系扩展方法，实验结果表明该方法在多个数据集上具有优异的效果。

利用文本和图形向量表示来丰富分类学

Taxonomy Enrichment with Text and Graph Vector Representations

Taxonomies have found wide applications in various domains, especially online
for item categorization, browsing, and search. Despite the prevalent use of
online catalog taxonomies, most of them in practice are maintained by humans,
which is labor-intensive and difficult to scale. While taxonomy construction
from scratch is considerably studied in the literature, how to effectively
enrich existing incomplete taxonomies remains an open yet important research
question. Taxonomy enrichment not only requires the robustness to deal with
emerging terms but also the consistency between existing taxonomy structure and
new term attachment. In this paper, we present a self-supervised end-to-end
framework, Octet, for Online Catalog Taxonomy EnrichmenT. Octet leverages
heterogeneous information unique to online catalog taxonomies such as user
queries, items, and their relations to the taxonomy nodes while requiring no
other supervision than the existing taxonomies. We propose to distantly train a
sequence labeling model for term extraction and employ graph neural networks
(GNNs) to capture the taxonomy structure as well as the query-item-taxonomy
interactions for term attachment. Extensive experiments in different online
domains demonstrate the superiority of Octet over state-of-the-art methods via
both automatic and human evaluations. Notably, Octet enriches an online catalog
taxonomy in production to 2 times larger in the open-world evaluation.

本文提出了一种名为 Octet 的自我监督端到端框架，用于在线目录分类法的增强，该框架利用了用户查询、物品及其与分类法节点的关系等在线目录分类法的异构信息，并采用序列标注模型进行术语提取，采用图神经网络来捕获分类法结构以及查询项与分类法的交互，实验表明 Octet 优于最先进的方法。