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Existing modeling languages lack the expressiveness or efficiency to support many modern and successful machine learning (ML) models such as structured prediction or matrix factorization. We present WOLFE, a probabilistic programming language that enables practitioners to develop such models. Most ML approaches can be formulated in terms of scalar(More)
This work describes the participation of the WBI-DDI team on the SemEval 2013 – Task 9.2 DDI extraction challenge. The task consisted of extracting interactions between pairs of drugs from two collections of documents (DrugBank and MEDLINE) and their classification into four subtypes: advise, effect, mechanism, and int. We developed a two-step approach in(More)
While most approaches to automatically recognizing entailment relations have used classifiers employing hand engineered features derived from complex natural language processing pipelines, in practice their performance has been only slightly better than bag-of-word pair classifiers using only lexical similarity. The only attempt so far to build an(More)
MOTIVATION The accurate identification of chemicals in text is important for many applications, including computer-assisted reconstruction of metabolic networks or retrieval of information about substances in drug development. But due to the diversity of naming conventions and traditions for such molecules, this task is highly complex and should be(More)
Matrix factorization approaches to relation extraction provide several attractive features: they support distant supervision, handle open schemas, and leverage unlabeled data. Unfortunately , these methods share a shortcoming with all other distantly supervised approaches: they cannot learn to extract target relations without existing data in the knowledge(More)
The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of(More)
Many machine reading approaches, from shallow information extraction to deep semantic parsing, map natural language to symbolic representations of meaning. Representations such as first-order logic capture the richness of natural language and support complex reasoning, but often fail in practice due to their reliance on logical background knowledge and the(More)
The BioCreative IV CHEMDNER Task provides participants with the opportunity to compare their methods for chemical named entity recognition (NER) and indexing in a controlled environment. We contributed to this task with our previous conditional random field based system [1] extended by a number of novel general and domain-specific features. For the latter,(More)
Named entity recognition (NER) systems are often based on machine learning techniques to reduce the labor-intensive development of hand-crafted extraction rules and domain-dependent dictionaries. Nevertheless, time-consuming feature engineering is often needed to achieve state-of-the-art performance. In this study, we investigate the impact of such(More)
In this paper we present a proof-of-concept implementation of Neural Theorem Provers (NTPs), end-to-end differentiable counterparts of discrete theorem provers that perform first-order inference on vector representations of symbols using function-free, possibly parameterized, rules. As such, NTPs follow a long tradition of neural-symbolic approaches to(More)