Structural representations

Xem 1-20 trên 181 kết quả Structural representations
  • A framework for a structured representation of semantic knowledge (e.g. word-senses) has been defined at the IBM Scientific Center of Roma, as part of a project on Italian Text Understanding. This representation, based on the conceptual graphs formalism [SOW84], expresses deep knowledge (pragmatic) on word-senses. The knowledge base data structure is such as to provide easy access by the semantic verification algorithm. This paper discusses some important problem related to the definition of a semantic knowledge base, as depth versus generality, hierarchical ordering of concept types, etc.

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  • Tham khảo sách 'mechanism design enumeration of kinematic structures according to function', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • In this paper we present an ambiguity preserving translation approach which transfers ambiguous LFG f-structure representations. It is based on packed f-structure representations which are the result of potentially ambiguous utterances. If the ambiguities between source and target language can be preserved, no unpacking during transfer is necessary and the generator may produce utterances which maximally cover the underlying ambiguities.

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  • Social scientists teach that politicians favor groups that are organized over those that are not. Representation through Taxation challenges this conventional wisdom. Emphasizing that there are limits to what organized interests can cred- ibly promise in return for favorable treatment, Gehlbach shows that politicians may instead give preference to groups – organized or not – that by their na- ture happen to take actions that are politically valuable.

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  • Social scientists teach that politicians favor groups that are organized over those that are not. Representation through Taxation challenges this conventional wisdom. Emphasizing that there are limits to what organized interests can cred- ibly promise in return for favorable treatment, Gehlbach shows that politicians may instead give preference to groups – organized or not – that by their na- ture happen to take actions that are politically valuable.

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  • Structural Representations of Mechanisms The kinematic structure of a mechanism contains the essential information about which link is connected to which other link by what type of joint. The kinematic structure of a mechanism can be represented in several different ways. Some methods of representation are fairly straightforward, whereas others may be rather abstract and do not necessarily have a one-to-one correspondence. In this chapter various methods of representation of the kinematic structure of a mechanism or kinematic chain are described.

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  • In recent years the subject of computer programming has been recognized as a discipline whose mastery is fundamental and crucial to the success of many engineering projects and which is amenable to scientific treatement and presentation. It has advanced from a craft to an academic discipline. The initial outstanding contributions toward this development were made by E.W. Dijkstra and C.A.R. Hoare.

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  • This paper presents empirical studies and closely corresponding theoretical models of the performance of a chart parser exhaustively parsing the Penn Treebank with the Treebank’s own CFG grammar. We show how performance is dramatically affected by rule representation and tree transformations, but little by top-down vs. bottom-up strategies.

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  • We present two approaches for syntactic and semantic transfer based on LFG f-structures and compare the results with existing co-description and restriction operator based approaches, focusing on aspects of ambiguity preserving transfer, complex cases of syntactic structural mismatches as well as on modularity and reusability. The two transfer approaches are interfaced with an existing, implemented transfer component (Verbmobi1), by translating f-structures into a term language, and by interfacing fstructure representations with an existing semantic based transfer approach, respectively. ...

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  • In this paper we deal with Named Entity Recognition (NER) on transcriptions of French broadcast data. Two aspects make the task more difficult with respect to previous NER tasks: i) named entities annotated used in this work have a tree structure, thus the task cannot be tackled as a sequence labelling task; ii) the data used are more noisy than data used for previous NER tasks. We approach the task in two steps, involving Conditional Random Fields and Probabilistic Context-Free Grammars, integrated in a single parsing algorithm.

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  • This paper establishes a connection between two apparently very different kinds of probabilistic models. Latent Dirichlet Allocation (LDA) models are used as “topic models” to produce a lowdimensional representation of documents, while Probabilistic Context-Free Grammars (PCFGs) define distributions over trees. The paper begins by showing that LDA topic models can be viewed as a special kind of PCFG, so Bayesian inference for PCFGs can be used to infer Topic Models as well. Adaptor Grammars (AGs) are a hierarchical, non-parameteric Bayesian extension of PCFGs. ...

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  • Finding a class of structures that is rich enough for adequate linguistic representation yet restricted enough for efficient computational processing is an important problem for dependency parsing. In this paper, we present a transition system for 2-planar dependency trees – trees that can be decomposed into at most two planar graphs – and show that it can be used to implement a classifier-based parser that runs in linear time and outperforms a stateof-the-art transition-based parser on four data sets from the CoNLL-X shared task. ...

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  • We present a probabilistic model extension to the Tesni` re Dependency Structure e (TDS) framework formulated in (Sangati and Mazza, 2009). This representation incorporates aspects from both constituency and dependency theory. In addition, it makes use of junction structures to handle coordination constructions. We test our model on parsing the English Penn WSJ treebank using a re-ranking framework.

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  • This paper introduces a machine learning method based on bayesian networks which is applied to the mapping between deep semantic representations and lexical semantic resources. A probabilistic model comprising Minimal Recursion Semantics (MRS) structures and lexicalist oriented semantic features is acquired. Lexical semantic roles enriching the MRS structures are inferred, which are useful to improve the accuracy of deep semantic parsing.

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  • Underspecification-based algorithms for processing partially disambiguated discourse structure must cope with extremely high numbers of readings. Based on previous work on dominance graphs and weighted tree grammars, we provide the first possibility for computing an underspecified discourse description and a best discourse representation efficiently enough to process even the longest discourses in the RST Discourse Treebank.

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  • In this paper we explore the utility of the Navigation Map (NM), a graphical representation of the discourse structure. We run a user study to investigate if users perceive the NM as helpful in a tutoring spoken dialogue system. From the users’ perspective, our results show that the NM presence allows them to better identify and follow the tutoring plan and to better integrate the instruction. It was also easier for users to concentrate and to learn from the system if the NM was present. Our preliminary analysis on objective metrics further strengthens these findings. ...

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  • Several approaches have been described for the automatic unsupervised acquisition of patterns for information extraction. Each approach is based on a particular model for the patterns to be acquired, such as a predicate-argument structure or a dependency chain. The effect of these alternative models has not been previously studied. In this paper, we compare the prior models and introduce a new model, the Subtree model, based on arbitrary subtrees of dependency trees.

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  • We introduce a first-order language for semantic underspecification that we call Constraint Language for Lambda-Structures (CLLS). A Astructure can be considered as a A-term up to consistent renaming of bound variables (aequality); a constraint of CLLS is an underspecified description of a A-structure. CLLS solves a capturing problem omnipresent in underspecified scope representations. CLLS features constraints for dominance, lambda binding, parallelism, and anaphoric links. Based on CLLS we present a simple, integrated, and underspecified treatment of scope, parallelism, and anaphora. ...

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  • A theory of discourse interpretation has to deal with a set of problems including anaphora resolution and the hierarchical ordering of discourse structure: (1) Several students organised a dinner party for Peter. Some students wrote fancy invitation cards. Some other students bought exotic food. But Peter didn't like it.

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  • This paper presents a system which automatically generates shallow semantic frame structures for conversational speech in unrestricted domains. We argue that such shallow semantic representations can indeed be generated with a minimum amount of linguistic knowledge engineering and without having to explicitly construct a semantic knowledge base.

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