Unsupervised extreme multi label classification of stack overflow posts

Devine, Peter; Blincoe, Kelly

doi:10.1145/3528588.3528652

Cited by 11 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It checks for overlap of semantic unit-based representation of a code with each sentence in a response. (Devine and Blincoe 2022) is an unsupervised approach for XMLC which tags posts on tech forums like StackOverflow with relevant categories. Embeddings of the post and category tags obtained using deep Transformer based models are compared using cosine similarity to obtain list of matching tags.…”

Section: Related Workmentioning

confidence: 99%

Dig Here! Extracting and Using Knowledge from Financial Audit Reports

Pawar

Apte

Pawde

et al. 2023

FLAIRS

View full text Add to dashboard Cite

Financial audits establish trust in the governance and processes in an organization, but they are time-consuming and knowledge intensive. To increase the effectiveness of financial audit, we address the task of generating audit suggestions that can help auditors to focus their investigations. Specifically, we present NLP techniques to extract hidden knowledge from a corpus of past financial audit reports of many companies, and use it for generating audit suggestions. The extracted knowledge consists of a set of automatically identified sentences containing adverse remarks, the financial variables mentioned in each sentence and automatically assigned XBRL categories for them, since XBRL is a standardized taxonomy in the financial domain. In the absence of suitable labeled data, we adopted a weak supervision approach. We designed a set of high precision linguistic rules to identify adverse remark sentences, created automatically labeled training data using them, and trained BERT-based and other classifiers to identify such sentences. We next presented novel techniques (which are either unsupervised or zero-shot) to assign zero, one, or more XBRL categories to any given adverse remark sentence. We evaluated the proposed approaches, on a large corpus of real-life financial statements and audit reports, against competent baselines. Given a company’s financial statements (already identified as suspicious), and given a subset of financial variables in them that contribute to suspiciousness, we match these with the extracted knowledge base and identify aligned adverse remarks that help the auditor in focusing on specific directions for further investigations.

show abstract

Section: Related Workmentioning

confidence: 99%

Dig Here! Extracting and Using Knowledge from Financial Audit Reports

Pawar

Apte

Pawde

et al. 2023

FLAIRS

View full text Add to dashboard Cite

show abstract

Are Unsupervised Text Classification Techniques Sufficient for Categorizing Short Texts like Product Names?

Mishra

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Automatic assignment of microgenres to movies using a word embedding-based approach

González-Santos,

Vega-Rodríguez,

López-Muñoz

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Streaming services are increasingly leveraging Artificial Intelligence (AI) technologies for improved content cataloging, user experiences in content discovery, and personalization. A significant challenge in this domain is the automated assignment of microgenres to movies. This study introduces and evaluates approaches based on clustering, topic modeling, and word embedding to address this task. The evaluation employs a preprocessed dataset containing movie-related data—title tags, synopses, genres, and reviews—alongside a predefined microgenre list. Comparisons of three activation functions (binary step, ramp, and sigmoid) gauge their effectiveness in augmenting microgenre tags. Results demonstrate the superiority of the word embedding approach over clustering and topic modeling in terms of mean accuracy. Even more, the word embedding approach stands as the sole fully automated solution. Analysis indicates that incorporating review-based tags introduces noise and undermines accuracy. Besides, the word embedding approach yields optimal outcomes using the sigmoid function, effectively doubling assigned tags while maintaining matching quality. This sheds light on the potential of word embedding methods within the movie domain.

show abstract

Unsupervised extreme multi label classification of stack overflow posts

Cited by 11 publications

References 25 publications

Dig Here! Extracting and Using Knowledge from Financial Audit Reports

Dig Here! Extracting and Using Knowledge from Financial Audit Reports

Are Unsupervised Text Classification Techniques Sufficient for Categorizing Short Texts like Product Names?

Automatic assignment of microgenres to movies using a word embedding-based approach

Contact Info

Product

Resources

About