Combining Embeddings and Domain Knowledge for Job Posting Duplicate Detection

Job descriptions are posted on many online channels, including company websites, job boards or social media platforms. These descriptions are usually published with varying text for the same job, due to the requirements of each platform or to target different audiences. However, for the purpose of automated recruitment and assistance of people working with these texts, it is helpful to aggregate job postings across platforms and thus detect duplicate descriptions that refer to the same job. In this work, we propose an approach for detecting duplicates in job descriptions. We show that combining overlap-based character similarity with text embedding and keyword matching methods lead to convincing results. In particular, we show that although no approach individually achieves satisfying performance, a combination of string comparison, deep textual embeddings, and the use of curated weighted lookup lists for specific skills leads to a significant boost in overall performance. A tool based on our approach is being used in production and feedback from real-life use confirms our evaluation.

Fine-tuning and aligning question answering models for complex information extraction tasks

The emergence of Large Language Models (LLMs) has boosted performance and possibilities in various NLP tasks. While the usage of generative AI models like ChatGPT opens up new opportunities for several business use cases, their current tendency to hallucinate fake content strongly limits their applicability to document analysis, such as information retrieval from documents. In contrast, extractive language models like question answering (QA) or passage retrieval models guarantee query results to be found within the boundaries of an according context document, which makes them candidates for more reliable information extraction in productive environments of companies. In this work we propose an approach that uses and integrates extractive QA models for improved feature extraction of German business documents such as insurance reports or medical leaflets into a document analysis solution. We further show that fine-tuning existing German QA models boosts performance for tailored extraction tasks of complex linguistic features like damage cause explanations or descriptions of medication appearance, even with using only a small set of annotated data. Finally, we discuss the relevance of scoring metrics for evaluating information extraction tasks and deduce a combined metric from Levenshtein distance, F1-Score, Exact Match and ROUGE-L to mimic the assessment criteria from human experts.

Combining Deep Learning and Reasoning for Address Detection in Unstructured Text Documents

Extracting information from unstructured text documents is a demanding task, since these documents can have a broad variety of different layouts and a non-trivial reading order, like it is the case for multi-column documents or nested tables. Additionally, many business documents are received in paper form, meaning that the textual contents need to be digitized before further analysis. Nonetheless, automatic detection and capturing of crucial document information like the sender address would boost many companies' processing efficiency. In this work we propose a hybrid approach that combines deep learning with reasoning for finding and extracting addresses from unstructured text documents. We use a visual deep learning model to detect the boundaries of possible address regions on the scanned document images and validate these results by analyzing the containing text using domain knowledge represented as a rule based system.