Database semantics

Xem 1-20 trên 56 kết quả Database semantics
  • Database System: Chapter 7 - Functional Dependencies includes Informal Design Guidelines for Relational Databases (Semantics of the Relation Attributes, Redundant Information in Tuples and Update Anomalies, Null Values in Tuples, Spurious Tuples), Functional Dependencies (FDs).

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  • Informal design guidelines for relational databases: Semantics of the relation attributes, reducing the redundant values in tuples, reducing the null values in tuples, disallowing the possibility of generating spurious tuples.

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  • This paper presents an open-domain textual Question-Answering system that uses several feedback loops to enhance its performance. These feedback loops combine in a new way statistical results with syntactic, semantic or pragmatic information derived from texts and lexical databases. The paper presents the contribution of each feedback loop to the overall performance of 76% human-assessed precise answers.

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  • Semantic parsing is the task of mapping natural language sentences into complete formal meaning representations which a computer can execute for some domain-specific application. This is a challenging task and is critical for developing computing systems that can understand and process natural language input, for example, a computing system that answers natural language queries about a database, or a robot that takes commands in natural language.

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  • This paper describes automatic techniques for mapping 9611 entries in a database of English verbs to WordNet senses. The verbs were initially grouped into 491 classes based on syntactic features. Mapping these verbs into WordNet senses provides a resource that supports disambiguation in multilingual applications such as machine translation and cross-language information retrieval.

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  • For a natural language access to database system to be practical it must achieve a good match between the capabilities of the user and the requirements of the task. The user brings his own natural language and his own style of interaction to the system. The task brings the questions that must be answered and the database domaln+s semantics. All natural language access systems achieve some degree of success. But to make progress as a field, we need to be able to evaluate the degree of this success. For too long, the best we have menaged has been to...

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  • This paper presents the first empirical results to our knowledge on learning synchronous grammars that generate logical forms. Using statistical machine translation techniques, a semantic parser based on a synchronous context-free grammar augmented with λoperators is learned given a set of training sentences and their correct logical forms. The resulting parser is shown to be the bestperforming system so far in a database query domain.

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  • In responding to the guidelines established by the session chairman of this panel, three of the five topics he set forth will be discussed. These include aggregate functions and quantity questions, querying semantically complex fields, and multi-file queries. As we will make clear in the sequel, the transformational apparatus utilized in the TQA Question Answering System provides a principled basis for handling these and many other problems i n natural language access to databases.

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  • Current Semantic Role Labeling technologies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based systems currently make use of the FrameNet database but fail to show suitable generalization capabilities in out-of-domain scenarios. In this paper, a state-of-art system for frame-based SRL is extended through the encapsulation of a distributional model of semantic similarity. The resulting argument classification model promotes a simpler feature space that limits the potential overfitting effects....

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  • We present a new approach to learning a semantic parser (a system that maps natural language sentences into logical form). Unlike previous methods, it exploits an existing syntactic parser to produce disambiguated parse trees that drive the compositional semantic interpretation. The resulting system produces improved results on standard corpora on natural language interfaces for database querying and simulated robot control.

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  • We present an API developed to access GermaNet, a lexical semantic database for German represented in XML. The API provides a set of software functions for parsing and retrieving information from GermaNet. Then, we present a case study which builds upon the GermaNet API and implements an application for computing semantic relatedness according to five different metrics. The package can, again, serve as a software library to be deployed in natural language processing applications. A graphical user interface allows to interactively experiment with the system. ...

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  • An approach is described for supplying selectional restrictions to parsers in natural language interfaces (NLIs) to databases by extracting the selectional restrictions from semantic descriptions of those NLIs. Automating the process of finding selectional restrictions reduces NLI development time and may avoid errors introduced by handcoding selectional restrictions.

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  • The knowledge representation is an important factor in natural language generation since it limits the semantic capabilities of the generation system. This paper identifies several information types in a knowledge representation that can be used to generate meaningful responses to questions about database structure. Creating such a knowledge representation, however, is a long and tedious process. A system is presented which uses the contents of the database to form part of this knowledge representation automatically. ...

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  • An improved version of IRACQ (for Interpretation Rule ACQuisition) is presented. I Our approach to semantic knowledge acquisition: 1 ) is in the context of a general purpose NL interface rather than one that accesses only databases, 2) employs a knowledge representation formalism with limited inferencing capabilities, 3) assumes a trained person but not an AI expert, and 4) provides a complete environment for not only acquiring semantic knowledge, but also maintaining and editing it in a consistent knowledge base. IRACQ is currently in use at the Naval Ocean Systems Center. ...

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  • Comparative expressions (CEs) such as "bigger than" and "more oranges than" are highly ambiguous, and their meaning is context dependent. Thus, they pose problems for the semantic interpretation algorithms typically used in natural language database interfaces. We focus on the comparison attribute ambiguities that occur with CEs. To resolve these ambiguities our natural language interface interacts with the user, finding out which of the possible interpretations was intended.

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  • One of the essential aspects is described of an expert system (called LEXICOGRAPItER), designed to supply the user with diverse brformation about Russian words, h~cluding bibliographic hiformation concemhrg hrdividual lexical entries. Tire lexical database of tire system contahrs semantic btfonnation that catmot be elicited from the existhrg dictionaries. Tire priority is given to semantic features influenchtg lexical or grammatical co-occurrence restrictions. Possibilities are discussed of predicting selectional restrictions on the basis of semantic features of a word bz the lexicon. ...

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  • This paper discusses an implemented program that automatically classifies verbs into those that ~describe only states of the world, such as to know, and those that describe events, such as to look. It works by exploiting the con, straint between the syntactic environments in which a verb can occur and its meaning. The only input is on-line text. This demonstrates an important new technique for the automatic generation of lexical databases.

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  • In this paper we provide a quantitative evaluation of information automatically extracted from machine readable dictionaries. Our results show that for any one dictionary, 55-70% of the extracted information is garbled in some way. However, we show that these results can be dramatically reduced to about 6% by combining the information extracted from five dictionaries. It therefore appears that even if individual dictionaries are an unreliable source of semantic information, multiple dictionaries can play an important role in building large lexical-semantic databases. ...

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  • The lexicons for Knowledge-Based Machine Translation systems require knowledge intensive morphological, syntactic and semantic information. This information is often used in different ways and usually formatted for a specific NLP system. This tends to make both the acquisition and maintenance of lexical databases cumbersome, inefficient and error-prone. In order to solve these problems, we have developed a program called COOL which automates the acquisition and maintenance processes and allows us to standardize and centralize the databases. ...

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  • Chapter 7: Functional Dependencies presents about Informal Design Guidelines for Relational Databases (Semantics of the Relation Attributes, Redundant Information in Tuples and Update Anomalies, Null Values in Tuples, Spurious Tuples), Functional Dependencies (FDs).

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