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A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as “seeds”, in order to automatically learn pattern rules from parsed data, which then can extract new instances of the relation and its projections. We propose a novel rule(More)
We present an implemented approach for domain-restricted question answering from structured knowledge sources, based on robust semantic analysis in a hybrid NLP system architecture. We perform question interpretation and answer extraction in an architecture that builds on a lexical-conceptual structure for question interpretation, which is interfaced with(More)
We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from(More)
In this paper, we present an unsupervised hybrid text-mining approach to automatic acquisition of domain relevant terms and their relations. We deploy the TFIDF-based term classification method to acquire domain relevant single-word terms. Further, we apply two strategies in order to learn lexico-syntatic patterns which indicate paradigmatic and domain(More)
We present an implemented approach for domainrestricted question answering from structured knowledge sources, based on robust semantic analysis in a hybrid NLP system architecture. We build on a lexicalsemantic conceptual structure for question interpretation, which is interfaced with domain-specific concepts and properties in a structured knowledge base.(More)
In this paper, we present an approach for automatically detecting events in natural language texts by learning patterns that signal the mentioning of such events. We construe the relevant event types as relations and start with a set of seeds consisting of representative event instances that happen to be known and also to be mentioned frequently in easily(More)
In this paper we describe an integrated approach to cross-language retrieval within the MIETTA project, whose objective is to build a special purpose search engine in the tourism domain that covers information from a number of geographical regions. MIETTA is designed to enable users to search and retrieve information on the regions covered in their own(More)
To alleviate the error propagation in the traditional pipelined models for Abstract Meaning Representation (AMR) parsing, we formulate AMR parsing as a joint task that performs the two subtasks: concept identification and relation identification simultaneously. To this end, we first develop a novel componentwise beam search algorithm for relation(More)
This paper presents a new approach to improving relation extraction based on minimally supervised learning. By adding some limited closed-world knowledge for confidence estimation of learned rules to the usual seed data, the precision of relation extraction can be considerably improved. Starting from an existing baseline system we demonstrate that utilizing(More)