Compositional question answering begins by mapping questions to logical forms, but training a semantic parser to perform this mapping typically requires the costly annotation of the target logical forms. In this paper, we learn to map questions to answers via latent logical forms, which are induced automatically from question-answer pairs. In tackling this challenging learning problem, we introduce a new semantic representation which highlights a parallel between dependency syntax and efﬁcient evaluation of logical forms. ...
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học 'Respiratory Research cung cấp cho các bạn kiến thức về ngành y đề tài: " Validation of a guideline-based composite outcome assessment tool for asthma control...
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Recombination and base composition: the case of the highly self-fertilizing plant Arabidopsis thaliana...
Composite applications aid businesses by stitching together various componented
business capabilities. In the current enterprise scenario, empowering business
users to react quickly to the rapidly changing business environment is the top most
priority. With the advent of composite applications the 'reuse' paradigm has moved
from the technical aspect to the business aspect. You no longer re-use a service
but re-use a business process. Now, enterprises can define their own behaviors
optimized for their businesses through metadata and flows.
SITE BASED MANAGEMENT: A DESIGN PERSPECTIVE The first two chapters consider parents choice of schools for their children. The
claim that parental choice can create incentives for schools to become more productive is a
tenet of the neoclassical analysis of education. It relies crucially on the assumption that
parents will choose effective, productive schools.
In this paper, a new language model, the Multi-Class Composite N-gram, is proposed to avoid a data sparseness problem for spoken language in that it is difﬁcult to collect training data. The Multi-Class Composite N-gram maintains an accurate word prediction capability and reliability for sparse data with a compact model size based on multiple word clusters, called MultiClasses. In the Multi-Class, the statistical connectivity at each position of the N-grams is regarded as word attributes, and one word cluster each is created to represent the positional attributes. ...
We describe novel aspects of a new natural language generator called Nitrogen. This generator has a highly flexible input representation that allows a spectrum of input from syntactic to semantic depth, and shifts' the burden of many linguistic decisions to the statistical post-processor. The generation algorithm is compositional, making it efficient, yet it also handles non-compositional aspects of language. Nitrogen's design makes it robust and scalable, operating with lexicons and knowledge bases of one hundred thousand entities. ...
A compositional account of the semantics of German prefix verbs in HPSG is outlined. We consider only those verbs that are formed by productive synchronic rules. Rules are fully productive if they apply to all base verbs which satisfy a common description. Prefixes can be polysemous and have separate, highly underspecified lexical entries. Adequate bases are determined via selection restrictions.
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.
We develop a framework for formalizing semantic construction within grammars expressed in typed feature structure logics, including HPSG. The approach provides an alternative to the lambda calculus; it maintains much of the desirable ﬂexibility of uniﬁcationbased approaches to composition, while constraining the allowable operations in order to capture basic generalizations and improve maintainability.
Non-compositional expressions present a special challenge to NLP applications. We present a method for automatic identification of non-compositional expressions using their statistical properties in a text corpus. Our method is based on the hypothesis that when a phrase is non-composition, its mutual information differs significantly from the mutual informations of phrases obtained by substituting one of the word in the phrase with a similar word.
CCG, one of the most prominent grammar frameworks, efﬁciently deals with deletion under coordination in natural languages. However, when we expand our attention to more analytic languages whose degree of pro-dropping is more free, CCG’s decomposition rule for dealing with gapping becomes incapable of parsing some patterns of intra-sentential ellipses in serial verb construction. Moreover, the decomposition rule might also lead us to overgeneration problem. In this paper the composition rule is replaced by the use of memory mechanism, called CCG-MM. ...
Within unification-based grammar formalisms, providing a treatment of cross-categorial coordination is problematic, and most current solutions either over-generate or under-generate. In this paper we consider an approach to coordination involving "composite" feature structures, which describe coordinate phrases, and present the augmentation to the logic of feature structures required to admit such feature structures.
Silane and NaOH were used to treatment bamboo shoot culm sheath fiber. After treatment, the interfacial shear strength of fiber with MAPP increased by 24% and 30% respectively. Alkali treatment has much effect on bamboo shoot culm sheath fiber than silane treatment. Washing NaOH treatment bamboo fiber with acetic acid was improved IFSS of bamboo fiber and polypropylene (PP) and strength of composite PP reinforced by bamboo fiber.
The main contents of this chapter include all of the following: Changing the size and composition of the balance sheet, open market operations, foreign exchange intervention, discount loans, cash withdrawal.
The pipeline of most Phrase-Based Statistical Machine Translation (PB-SMT) systems starts from automatically word aligned parallel corpus. But word appears to be too fine-grained in some cases such as non-compositional phrasal equivalences, where no clear word alignments exist. Using words as inputs to PBSMT pipeline has inborn deficiency. This paper proposes pseudo-word as a new start point for PB-SMT pipeline.
We propose CMSMs, a novel type of generic compositional models for syntactic and semantic aspects of natural language, based on matrix multiplication. We argue for the structural and cognitive plausibility of this model and show that it is able to cover and combine various common compositional NLP approaches ranging from statistical word space models to symbolic grammar formalisms.
This paper proposes a framework for representing the meaning of phrases and sentences in vector space. Central to our approach is vector composition which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models which we evaluate empirically on a sentence similarity task. Experimental results demonstrate that the multiplicative models are superior to the additive alternatives when compared against human judgments. ...
Given several systems’ automatic translations of the same sentence, we show how to combine them into a confusion network, whose various paths represent composite translations that could be considered in a subsequent rescoring step. We build our confusion networks using the method of Rosti et al. (2007), but, instead of forming alignments using the tercom script (Snover et al., 2006), we create alignments that minimize invWER (Leusch et al., 2003), a form of edit distance that permits properly nested block movements of substrings. ...