+ Attributes là một thành phần thêm vào được dùng để khai báo thêm thông tin về các lớp, phương thức, thuộc tính, kiểu … + Attributes có thể được áp dụng nên các phần tử khác nhau của chương trình. Các phần tử đó bao gồm:
In this activity, you will continue to analyze the selected future-state usage
scenario focusing on the business object attributes and relationships.
First, you will identify the attributes of the business objects.
Next, you will identify the relationships between the business objects. Microsoft Official Curriculum (MOC), available to IT Academies at a discounted price, is professional courseware intended for IT professionals and developers who build, support, and implement solutions by using Microsoft products and technologies.
We investigate authorship attribution using classiﬁers based on frame semantics. The purpose is to discover whether adding semantic information to lexical and syntactic methods for authorship attribution will improve them, speciﬁcally to address the difﬁcult problem of authorship attribution of translated texts.
An attractive property of attribute-value grammars is their reversibility. Attribute-value grammars are usually coupled with separate statistical components for parse selection and ﬂuency ranking. We propose reversible stochastic attribute-value grammars, in which a single statistical model is employed both for parse selection and ﬂuency ranking.
This paper deals with an application of automatic titling. The aim of such application is to attribute a title for a given text. So, our application relies on three very different automatic titling methods. The ﬁrst one extracts relevant noun phrases for their use as a heading, the second one automatically constructs headings by selecting words appearing in the text, and, ﬁnally, the third one uses nominalization in order to propose informative and catchy titles. Experiments based on 1048 titles have shown that our methods provide relevant titles. ...
Authorship attribution deals with identifying the authors of anonymous texts. Building on our earlier ﬁnding that the Latent Dirichlet Allocation (LDA) topic model can be used to improve authorship attribution accuracy, we show that employing a previously-suggested Author-Topic (AT) model outperforms LDA when applied to scenarios with many authors.
In this paper, we present a novel approach for authorship attribution, the task of identifying the author of a document, using probabilistic context-free grammars. Our approach involves building a probabilistic context-free grammar for each author and using this grammar as a language model for classiﬁcation. We evaluate the performance of our method on a wide range of datasets to demonstrate its efﬁcacy. (2008) use a combination of word-level statistics and part-of-speech counts or n-grams. ...
Automatic opinion recognition involves a number of related tasks, such as identifying the boundaries of opinion expression, determining their polarity, and determining their intensity. Although much progress has been made in this area, existing research typically treats each of the above tasks in isolation. In this paper, we apply a hierarchical parameter sharing technique using Conditional Random Fields for ﬁne-grained opinion analysis, jointly detecting the boundaries of opinion expressions as well as determining two of their key attributes — polarity and intensity. ...
This paper proposes the use of local histograms (LH) over character n-grams for authorship attribution (AA). LHs are enriched histogram representations that preserve sequential information in documents; they have been successfully used for text categorization and document visualization using word histograms. In this work we explore the suitability of LHs over n-grams at the character-level for AA. We show that LHs are particularly helpful for AA, because they provide useful information for uncovering, to some extent, the writing style of authors.
This paper presents and evaluates several original techniques for the latent classiﬁcation of biographic attributes such as gender, age and native language, in diverse genres (conversation transcripts, email) and languages (Arabic, English). First, we present a novel partner-sensitive model for extracting biographic attributes in conversations, given the differences in lexical usage and discourse style such as observed between same-gender and mixedgender conversations.
The syntactic analysis of languages with respect to Government-binding (GB) grammar is a problem that has received relatively little attention until recently. This paper describes an attribute grammar specification of the Government-binding theory. The paper focuses on the description of the attribution rules responsible for determining antecedent-trace relations in phrase-structure trees, and on some theoretical implications of those rules for the G B model.
In this paper we present an approach to automatic authorship attribution dealing with real-world (or unrestricted) text. Our method is based on the computational analysis of the input text using a text-processing tool. Besides the style markers relevant to the output of this tool we also use analysis-dependent style markers, that is, measures that represent the way in which the text has been processed. No word frequency counts, nor other lexically-based measures are taken into account.
We present a novel framework for automated extraction and approximation of numerical object attributes such as height and weight from the Web. Given an object-attribute pair, we discover and analyze attribute information for a set of comparable objects in order to infer the desired value. This allows us to approximate the desired numerical values even when no exact values can be found in the text.
This paper presents a set of Bayesian methods for automatically extending the W ORD N ET ontology with new concepts and annotating existing concepts with generic property ﬁelds, or attributes. We base our approach on Latent Dirichlet Allocation and evaluate along two dimensions: (1) the precision of the ranked lists of attributes, and (2) the quality of the attribute assignments to W ORD N ET concepts. In all cases we ﬁnd that the principled LDA-based approaches outperform previously proposed heuristic methods, greatly improving the speciﬁcity of attributes at each concept. ...
A new approach to large-scale information extraction exploits both Web documents and query logs to acquire thousands of opendomain classes of instances, along with relevant sets of open-domain class attributes at precision levels previously obtained only on small-scale, manually-assembled classes. a m
We present a method for computerassisted authorship attribution based on character-level n-gram language models. Our approach is based on simple information theoretic principles, and achieves improved performance across a variety of languages without requiring extensive pre-processing or feature selection.
A set of labeled classes of instances is extracted from text and linked into an existing conceptual hierarchy. Besides a significant increase in the coverage of the class labels assigned to individual instances, the resulting resource of labeled classes is more effective than similar data derived from the manually-created Wikipedia, in the task of attribute extraction over conceptual hierarchies.
This paper describes a classical logic for attribute-value (or feature description) languages which ate used in urfification grammar to describe a certain kind of linguistic object commonly called attribute-value structure (or feature structure). Tile algorithm which is used for deciding satisfiability of a feature description is based on a restricted deductive closure construction for sets of literals (atomic formulas and negated atomic formulas). In contrast to the Kasper/Rounds approach (cf.
Attribute grammars are an elegant formalization of the augmented context-free grammars characteristic of most current natural language systems. This paper presents an extension of Earley's algorithm to Knuth's attribute grammars, considering the case of S-attributed grammars. For this case, we study the conditions on the underlying base grammar under which the extended algorithm may be guaranteed to terminate. Finite partitioning of attribute domains is proposed to guarantee the termination of the algorithm, without the need for any restrictions on the context-free base. ...
After you have mastered the material in this chapter, you will be able to: Identify the objectives of attributes sampling, define deviation conditions, and define the population for an attributes sampling application; understand how various factors influence the size of an attributes sample; determine the sample size for an attributes sampling application;...