Papers by Martin Riedl

2 papers
A Named Entity Recognition Shootout for German (P18-2)

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Challenge: Named entity recognition and classification (NER) is a central component in many natural language processing pipelines.
Approach: They propose to build a model for German named entity recognition that performs at the state of the art for both contemporary and historical texts.
Outcome: The proposed model outperforms the CRF and BiLSTM on large and small datasets.
Document-based Recommender System for Job Postings using Dense Representations (N18-3)

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Challenge: 45% of job posting traffic is driven by recommender systems for job postings . a large-scale job recommendation system is needed to detect similarity between job posting and item-to-item based recommendations.
Approach: They propose to use dense vector representations to enhance a large-scale job recommendation system and rank job advertisements regarding similarity.
Outcome: The proposed method increases the click-through rate on job recommendations by 8.0%.

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