Papers in this group
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This paper describes a new automatic method for Japanese predicate argument structure analysis. The method learns relevant features to assign case roles to the argument of the target predicate using the features of the words located closest to the…
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The Information extraction can be defined as the task of extracting information of specified events or facts, and then stored in a database for the users' querying. Only with the correct relationship between the various entities, the database can be…
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Domain ontologies very rarely model verbs as relations holding be- tween concepts. However, the role of the verb as a central connecting element between concepts is undeniable. Verbs specify the interaction between the par- ticipants of some action…
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There have been major advances on automatically constructing large knowledge bases by extracting relational facts from Web and text sources. However, the world is dynamic: periodic events like sports competitions need to be interpreted with their…
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Due to the ever growing amount of publications, Information Extraction (IE) from text is increasingly is recognized as one of crucial technologies in bioinformatics. However, for IE to be practically applicable, adaptability/portability of a system…
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We present a novel spoken dialogue system which uses the up-to-date information on the web. It is based on information extraction which is defined by the predicate- argument (P-A) structure and realized by shallow parsing. Based on the information…
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This paper presents a method of automatically constructing information extraction patterns on predicate-argument structures (PASs) obtained by full parsing from a smaller training corpus. Because PASs represent generalized structures for syntactical…
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Introduction: Information Extraction (IE) systems are designed to extract fixed types of information from documents in a specific language and domain (Cowie and Lehnert, 1996; Appelt, 1999; Cunningham, 1999). Part of this task is coreference…
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We propose a novel approach to find aliases of a given name from the web. We exploit a set of known names and their aliases as training data and extract lexical patterns that convey information related to aliases of names from text snippets returned…
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Several machine learning techniques have been applied to the named entity (NE) recognition problem. However, there has been less progress on the problem of identifying relations between them; an important process in Information Extraction. This…
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Recent work on Semantic Role Labeling (SRL) has shown that syntactic information is critical to detect and extract predicate argument structures. As syntax is expressed by means of structured data, i.e. parse trees, its encoding in learning…
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A predicate-argument structure is composed of a predicate that represents a state or an event, and its arguments which have logical relations to the predicate. The logical relations are roles of arguments such as Agent, Theme, etc., are called…
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Biomedical entity extraction from unstructured web documents is an important task that needs to be performed in order to discover knowledge in the veterinary medicine domain. In general, this task can be approached by applying domain- specific…
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Coreference resolution is governed by syntactic, semantic, and discourse constraints. We present a generative, model-based approach in which each of these factors is modularly encapsulated and learned in a primarily unsupervised manner. Our semantic…
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Since 1995, a few statistical parsing algorithms have demonstrated a breakthrough in parsing accuracy, as measured against the UPenn TREEBANK as a gold standard. In this paper we report adapting a lexicalized, probabilistic context-free parser to…
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Archived web data is a great resource for scienti c research, but poses serious challenges in data processing and management. We demonstrate the Web Lab Collaboration Server, a platform and service for large-scale collaborative web data analysis in…
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De nition extraction can be useful for the creation of glossaries and in question answering systems. It is a tedious task to extract such sentences manually, and thus an automatic system is desirable. In this work we review various attempts at…
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There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and…
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SEKE is a semantic expectation-based knowledge extraction system for extracting causation knowledge from natural language texts. It is inspired by human behavior on analyzing texts and capturing information with semantic expectations. The framework…