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Published literature in molecular genetics may collectively provide much information on gene regulation networks. Dedicated computational approaches are required to sip through large volumes of text and infer gene interactions. We propose a novel sieve-based relation extraction system that uses linear-chain conditional random fields and rules. Also, we(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)
Received (received date) Revised (revised date) Accepted (day month year) Communicated by (xxxxxxxxxx) Large software projects are among most sophisticated human-made systems consisting of a network of interdependent parts. Past studies of software systems from the perspective of complex networks have already led to notable discoveries with different(More)
The basic indicators of a researcher's productivity and impact are still the number of publications and their citation counts. These metrics are clear, straightforward, and easy to obtain. When a ranking of scholars is needed, for instance in grant, award, or promotion procedures, their use is the fastest and cheapest way of prioritizing some scientists(More)
Traditional information extraction (IE) tasks roughly consist of named-entity recognition, relation extraction and coreference resolution. Much work in this area focuses primarily on separate subtasks where best performance can be achieved only on specialized domains. In this paper we present a collective IE approach combining all three tasks by employing(More)
Machine understanding of textual documents has been challenging since the early computer era. Since the information extraction research field emerged it has inferred multiple natural language processing tasks, such as named entities recognition, relationships extraction and coreference resolution. Even though for the purpose of the end-to-end information(More)
Due to numerous public information sources and services, many methods to combine heterogeneous data were proposed recently. However, general end-to-end solutions are still rare, especially systems taking into account different context dimensions. Therefore, the techniques often prove insufficient or are limited to a certain domain. In this paper we briefly(More)
Information Extraction is a process of extracting stru-ctured data from unstructured sources. It roughly consists of tasks like entity extraction, relation extraction and coreference resolution. Most of the current research focuses only on one of the tasks or their combination in a pipeline. In this paper we introduce an end-to-end iterative information(More)
Information Extraction (IE) is a process of extracting struc-tured data from unstructured sources. It roughly consists of subtasks named entity recognition, relation extraction and coreference resolution. Researchers have primarily focused just on one subtask or their combination in a pipeline. In this paper we introduce an intelligent collective IE system(More)
Slavko Žitnik Iterative semantic information extraction from unstructured text sources Nowadays we generate an enormous amount of data and most of it is unstructured. The users of Internet post more than , text documents and together write more than  million e-mails online every single minute. We would like to access this data in a structured form(More)