This paper shows how ﬁnite approximations of long distance dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II treebank trees and use them in an LDD resolution algorithm to parse new text.
In this paper the functional uncertainty machinery in L F G is compared with the treatment of long distance dependencies in TAG. It is shown that the functional uncertainty machinery is redundant in TAG, i.e.,what functional uncertainty accomplishes for L F G follows f~om the T A G formalism itself and some aspects of the linguistic theory instantiated in TAG. It is also shown that the analyses provided by the functional uncertainty machinery can be obtained without requiring power beyond mildly context-sensitive grammars.
The paper investigates the problem of providing a formal device for the dependency approach to syntax, and to link it with a parsing model. After reviewing the basic tenets of the paradigm and the few existing mathematical results, we describe a dependency formalism which is able to deal with long-distance dependencies. Finally, we present an Earley-style parser for the formalism and discuss the (polynomial) complexity results.
This paper will discuss how to determine word stress from spelling. Stress assignment is a well-established weak point for many speech synthesizers because stress dependencies cannot be determined locally. It is impossible to determine the stress of a word by looking through a five or six character window, as many speech synthesizers do. Wellknown examples such as degrade / dbgradl,tion and tMegraph / telegraph5 demonstrate that stress dependencies can span over two and three syllables. This paper will pre~nt a principled framework for dealing with these long distance dependencies.
This paper presents a dependency language model (DLM) that captures linguistic constraints via a dependency structure, i.e., a set of probabilistic dependencies that express the relations between headwords of each phrase in a sentence by an acyclic, planar, undirected graph. Our contributions are three-fold. First, we incorporate the dependency structure into an n-gram language model to capture long distance word dependency. Second, we present an unsupervised learning method that discovers the dependency structure of a sentence using a bootstrapping procedure. ...
Measure words in Chinese are used to indicate the count of nouns. Conventional statistical machine translation (SMT) systems do not perform well on measure word generation due to data sparseness and the potential long distance dependency between measure words and their corresponding head words. In this paper, we propose a statistical model to generate appropriate measure words of nouns for an English-to-Chinese SMT system. We model the probability of measure word generation by utilizing lexical and syntactic knowledge from both source and target sentences. ...
Strictly Piecewise (SP) languages are a subclass of regular languages which encode certain kinds of long-distance dependencies that are found in natural languages. Like the classes in the Chomsky and Subregular hierarchies, there are many independently converging characterizations of the SP class (Rogers et al., to appear). Here we deﬁne SP distributions and show that they can be efﬁciently estimated from positive data.
In this paper, we propose a novel string-todependency algorithm for statistical machine translation. With this new framework, we employ a target dependency language model during decoding to exploit long distance word relations, which are unavailable with a traditional n-gram language model. Our experiments show that the string-to-dependency decoder achieves 1.48 point improvement in BLEU and 2.53 point improvement in TER compared to a standard hierarchical string-tostring system on the NIST 04 Chinese-English evaluation set. ...
This paper shows that a simple two-stage approach to handle non-local dependencies in Named Entity Recognition (NER) can outperform existing approaches that handle non-local dependencies, while being much more computationally efﬁcient. NER systems typically use sequence models for tractable inference, but this makes them unable to capture the long distance structure present in text.
We show how unification can be used to specify the semantic interpretation of natural-language expressions, including problematical constructions involving long-distance dependencies. We also sketch a theoretical foundation for unificationbased semantic interpretation, and compare the unification-based approach with more conventional techniques based on the lambda calculus.
The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words-binary-parse-structure with headword annotation. The model, its probabilistic parametrization, and a set of experiments meant to evaluate its predictive power are presented.
This textbook, like all textbooks, was born of necessity. When I went looking
for a suitable textbook for my course on Lexical-Functional Grammar at the
Hebrew University of Jerusalem, I discovered that there wasn’t one. So I
decided to write one, based on my lecture notes. The writing accelerated
when, while I was on sabbatical at Stanford University (August 1999–
February 2000), Dikran Karagueuzian of CSLI Publications expressed
interest in publishing it.
In this paper, with a belief that a language model that embraces a larger context provides better prediction ability, we present two extensions to standard n-gram language models in statistical machine translation: a backward language model that augments the conventional forward language model, and a mutual information trigger model which captures long-distance dependencies that go beyond the scope of standard n-gram language models.
Understanding the flow pattern of benefits from ecosystems to people is a problem that has eluded past work in ecosystem services. For many authors, the flow problem has been expressed as a “spatial mismatch” between ecosystem service provision and use (Hein et al. 2006, Costanza 2008). By explicitly demonstrating spatial links from ecosystems to people and the strength of the flow of ecosystem services, we can better demonstrate how specific beneficiary groups gain value from ecosystem services.
We think the parts are of interest in their o~. The paper consists of three sections: (I) We give a detailed description of the PROLOG implementation of the parser which is based on the theory of lexical functional grammar (I/V.). The parser covers the fragment described in [1,94]. I.e., it is able to analyse constructions involving functional control and long distance dependencies.
In this paper, we exploit non-local features as an estimate of long-distance dependencies to improve performance on the statistical spoken language understanding (SLU) problem. The statistical natural language parsers trained on text perform unreliably to encode non-local information on spoken language. An alternative method we propose is to use trigger pairs that are automatically extracted by a feature induction algorithm. We describe a light version of the inducer in which a simple modiﬁcation is efﬁcient and successful. ...
This paper presents an application of ﬁnite state transducers weighted with feature structure descriptions, following Amtrup (2003), to the morphology of the Semitic language Tigrinya. It is shown that feature-structure weights provide an efﬁcient way of handling the templatic morphology that characterizes Semitic verb stems as well as the long-distance dependencies characterizing the complex Tigrinya verb morphotactics. A relatively complete computational implementation of Tigrinya verb morphology is described. ...
This paper describes a computational model of human sentence processing based on the principles and parameters paradigm of current linguistic theory. The syntactic processing model posits four modules, recovering phrase structure, long-distance dependencies, coreference, and thematic structure. These four modules are implemented as recta-interpreters over their relevant components of the grammar, permitting variation in the deductive strategies employed by each module.
Transmitter and Oscillator Systems
A transmitter is an important subsystem in a wireless system. In any active wireless system, a signal will be generated and transmitted through an antenna. The signal’s generating system is called a transmitter. The speciﬁcations for a transmitter depend on the applications. For long-distance transmission, high power and low noise are important. For space or battery operating systems, high efﬁciency is essential. For communication systems, low noise and good stability are required....
The entire format, name, route and length of your event depends solely on what you want to achieve.
You’re in charge of how complex or simple you want to make it. There are masses of different types of
walking event, ranging from fun walks and sponsored charity walks, to courses with measured
distances and long distance challenges.
A promoted walking event can be very attractive to people who are unused to walking or to exploring
the countryside, since they know they can’t get lost, they can go at their own pace, they may meet new
friends, learn more...