The Experts below are selected from a list of 34806 Experts worldwide ranked by ideXlab platform
Philipp Cimiano - One of the best experts on this subject based on the ideXlab platform.
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Automatic Acquisition of ranked qualia structures from the web
Meeting of the Association for Computational Linguistics, 2007Co-Authors: Philipp Cimiano, Johanna WenderothAbstract:This paper presents an approach for the Automatic Acquisition of qualia structures for nouns from the Web and thus opens the possibility to explore the impact of qualia structures for natural language processing at a larger scale. The approach builds on earlier work based on the idea of matching specific lexico-syntactic patterns conveying a certain semantic relation on the World Wide Web using standard search engines. In our approach, the qualia elements are actually ranked for each qualia role with respect to some measure. The specific contribution of the paper lies in the extensive analysis and quantitative comparison of different measures for ranking the qualia elements. Further, for the first time, we present a quantitative evaluation of such an approach for learning qualia structures with respect to a handcrafted gold standard.
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Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research, 2005Co-Authors: Philipp Cimiano, Andreas Hotho, Steffen StaabAbstract:We present a novel approach to the Automatic Acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are Automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.
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Automatic Acquisition of taxonomies from text fca meets nlp
European conference on Machine Learning, 2003Co-Authors: Philipp Cimiano, Steffen Staab, Julien TaneAbstract:We present a novel approach to the Automatic Acquisition of taxonomies or concept hierarchies from domain-specific texts based on Formal Concept Analysis (FCA). Our approach is based on the assumption that verbs pose more or less strong selectional restrictions on their arguments. The conceptual hierarchy is then built on the basis of the inclusion relations between the extensions of the selectional restrictions of all the verbs, while the verbs themselves provide intensional descriptions for each concept. We formalize this idea in terms of FCA and show how our approach can be used to acquire a concept hierarchy for the tourism domain out of texts. We then evaluate our method by considering an already existing ontology for this domain.
Dan I Moldovan - One of the best experts on this subject based on the ideXlab platform.
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Acquisition of linguistic patterns for knowledge based information extraction
IEEE Transactions on Knowledge and Data Engineering, 1995Co-Authors: Dan I MoldovanAbstract:The paper presents an Automatic Acquisition of linguistic patterns that can be used for knowledge based information extraction from texts. In knowledge based information extraction, linguistic patterns play a central role in the recognition and classification of input texts. Although the knowledge based approach has been proved effective for information extraction on limited domains, there are difficulties in construction of a large number of domain specific linguistic patterns. Manual creation of patterns is time consuming and error prone, even for a small application domain. To solve the scalability and the portability problem, an Automatic Acquisition of patterns must be provided. We present the PALKA (Parallel Automatic Linguistic Knowledge Acquisition) system that acquires linguistic patterns from a set of domain specific training texts and their desired outputs. A specialized representation of patterns called FP structures has been defined. Patterns are constructed in the form of FP structures from training texts, and the acquired patterns are tuned further through the generalization of semantic constraints. Inductive learning mechanism is applied in the generalization step. The PALKA system has been used to generate patterns for our information extraction system developed for the fourth Message Understanding Conference (MUC-4). >
Steffen Staab - One of the best experts on this subject based on the ideXlab platform.
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Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research, 2005Co-Authors: Philipp Cimiano, Andreas Hotho, Steffen StaabAbstract:We present a novel approach to the Automatic Acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are Automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.
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Automatic Acquisition of taxonomies from text fca meets nlp
European conference on Machine Learning, 2003Co-Authors: Philipp Cimiano, Steffen Staab, Julien TaneAbstract:We present a novel approach to the Automatic Acquisition of taxonomies or concept hierarchies from domain-specific texts based on Formal Concept Analysis (FCA). Our approach is based on the assumption that verbs pose more or less strong selectional restrictions on their arguments. The conceptual hierarchy is then built on the basis of the inclusion relations between the extensions of the selectional restrictions of all the verbs, while the verbs themselves provide intensional descriptions for each concept. We formalize this idea in terms of FCA and show how our approach can be used to acquire a concept hierarchy for the tourism domain out of texts. We then evaluate our method by considering an already existing ontology for this domain.
Pierre Jannin - One of the best experts on this subject based on the ideXlab platform.
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Automatic knowledge based recognition of low level tasks in ophthalmological procedures
Computer Assisted Radiology and Surgery, 2013Co-Authors: Florent Lalys, David Bouget, Laurent Riffaud, Pierre JanninAbstract:Purpose Surgical process models (SPMs) have recently been created for situation-aware computer-assisted systems in the operating room. One important challenge in this area is the Automatic Acquisition of SPMs. The purpose of this study is to present a new method for the Automatic detection of low-level surgical tasks, that is, the sequence of activities in a surgical procedure, from microscope video images only. The level of granularity that we addressed in this work is symbolized by activities formalized by triplets .
Carol Y. Espy-wilson - One of the best experts on this subject based on the ideXlab platform.
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Automatic Acquisition device identification from speech recordings
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Daniel Garcia-romero, Carol Y. Espy-wilsonAbstract:In this paper we present a study on the Automatic identification of Acquisition devices when only access to the output speech recordings is possible. A statistical characterization of the frequency response of the device contextualized by the speech content is proposed. In particular, the intrinsic characteristics of the device are captured by a template, constructed by appending together the means of a Gaussian mixture trained on the device speech recordings. This study focuses on two classes of Acquisition devices, namely, landline telephone handsets and microphones. Three publicly available databases are used to assess the performance of linear- and mel-scaled cepstral coefficients. A Support Vector Machine classifier was used to perform closed-set identification experiments. The results show classification accuracies higher than 90 percent among the eight telephone handsets and eight microphones tested.
