Rethinking log parsing in the context of modern software ecosystems

Modern systems generate a tremendous amount of data, making manual investigations infeasible, hence requiring automating the process of analysis. However, running automated log analysis pipelines is far from straightforward, due to the changing nature of software ecosystems caused by the constant need to adapt to user requirements. In practice,...

Semantic-Enhanced Training Data Augmentation Methods for Long-Tail Entity Recognition Models

Named Entity Recognition (NER) is an essential information retrieval task. It enables a wide range of natural language processing applications such as semantic search, machine translation, etc. The NER can be formulated as the task of identifying and typing words or phrases in a text that refers to certain classes of interest (e.g., disease,...

Automatic Generation of Legally and Ethically Correct Email Replies

In this paper, we will explain how our research into a classified field resulted in the creation of an entity recognizer that can recognize 10 different characteristics, and an intent classifier which is able to classify 21 different intents and automatically generate a response to incoming emails. This is all done within legal and ethical...

Coner: A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications

Named Entity Recognition (NER) for rare long-tail entities as e.g. often found in domain-specific scientific publications is a challenging task, as typically the extensive training data and test data for fine-tuning NER algorithms is lacking. Recent approaches presented promising solutions relying on training NER algorithms in a iterative...