# Out-of-Category Document Identification Using Target-Category Names as Weak Supervision

arXiv: https://arxiv.org/abs/2111.12796 · HTML: https://mlsanity.com/paper/2111.12796/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 18 of 18)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Pretrained Transformers Improve Out-of-Distribution Robustness](https://mlsanity.com/paper/2004.06100/) (2020)
- [Hyperbolic Entailment Cones for Learning Hierarchical Embeddings](https://mlsanity.com/paper/1804.01882/) (2018)
- [Order-Embeddings of Images and Language](https://mlsanity.com/paper/1511.06361/) (2015)
- [Word Representations via Gaussian Embedding](https://mlsanity.com/paper/1412.6623/) (2014)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [Spherical Text Embedding](https://mlsanity.com/paper/1911.01196/) (2019)
- [MASKER: Masked Keyword Regularization for Reliable Text Classification](https://mlsanity.com/paper/2012.09392/) (2020)
- [DATE: Detecting Anomalies in Text via Self-Supervision of Transformers](https://mlsanity.com/paper/2104.05591/) (2021)
- [Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding](https://mlsanity.com/paper/2007.09536/) (2020)
- [Multi-Class Data Description for Out-of-distribution Detection](https://mlsanity.com/paper/2104.00941/) (2021)
- [Outlier Detection for Text Data : An Extended Version](https://mlsanity.com/paper/1701.01325/) (2017)

## Built on by (most cited first, 1 of 1)

- [TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters](https://mlsanity.com/paper/2201.06771/) (2022)

## Abstract (arXiv)

Identifying outlier documents, whose content is different from the majority of the documents in a corpus, has played an important role to manage a large text collection. However, due to the absence of explicit information about the inlier (or target) distribution, existing unsupervised outlier detectors are likely to make unreliable results depending on the density or diversity of the outliers in the corpus. To address this challenge, we introduce a new task referred to as out-of-category detection, which aims to distinguish the documents according to their semantic relevance to the inlier (or target) categories by using the category names as weak supervision. In practice, this task can be widely applicable in that it can flexibly designate the scope of target categories according to users' interests while requiring only the target-category names as minimum guidance. In this paper, we present an out-of-category detection framework, which effectively measures how confidently each document belongs to one of the target categories based on its category-specific relevance score. Our framework adopts a two-step approach; (i) it first generates the pseudo-category label of all unlabeled documents by exploiting the word-document similarity encoded in a text embedding space, then (ii) it trains a neural classifier by using the pseudo-labels in order to compute the confidence from its target-category prediction. The experiments on real-world datasets demonstrate that our framework achieves the best detection performance among all baseline methods in various scenarios specifying different target categories.