Learning for semantic parsing using statistical syntactic parsing techniques

textNatural language understanding is a sub-field of natural language processing, which builds automated systems to understand natural language. It is such an ambitious task that it sometimes is referred to as an AI-complete problem, implying that its difficulty is equivalent to solving the central artificial intelligence problem -- making computers as intelligent as people. Despite its complexity, natural language understanding continues to be a fundamental problem in natural language processing in terms of its theoretical and empirical importance. In recent years, startling progress has been made at different levels of natural language processing tasks, which provides great opportunity for deeper natural language understanding. In this thesis, we focus on the task of semantic parsing, which maps a natural language sentence into a complete, formal meaning representation in a meaning representation language. We present two novel state-of-the-art learned syntax-based semantic parsers using statistical syntactic parsing techniques, motivated by the following two reasons. First, the syntax-based semantic parsing is theoretically well-founded in computational semantics. Second, adopting a syntax-based approach allows us to directly leverage the enormous progress made in statistical syntactic parsing. The first semantic parser, Scissor, adopts an integrated syntactic-semantic parsing approach, in which a statistical syntactic parser is augmented with semantic parameters to produce a semantically-augmented parse tree (SAPT). This integrated approach allows both syntactic and semantic information to be available during parsing time to obtain an accurate combined syntactic-semantic analysis. The performance of Scissor is further improved by using discriminative reranking for incorporating non-local features. The second semantic parser, SynSem, exploits an existing syntactic parser to produce disambiguated parse trees that drive the compositional semantic interpretation. This pipeline approach allows semantic parsing to conveniently leverage the most recent progress in statistical syntactic parsing. We report experimental results on two real applications: an interpreter for coaching instructions in robotic soccer and a natural-language database interface, showing that the improvement of Scissor and SynSem over other systems is mainly on long sentences, where the knowledge of syntax given in the form of annotated SAPTs or syntactic parses from an existing parser helps semantic composition. SynSem also significantly improves results with limited training data, and is shown to be robust to syntactic errors.Computer Science

Molecular Cloning, Characterization and Expression Analysis of Two Members of the Pht1 Family of Phosphate Transporters in Glycine max

BACKGROUND: Phosphorus is one of the macronutrients essential for plant growth and development. The acquisition and translocation of phosphate are pivotal processes of plant growth. In a large number of plants, phosphate uptake by roots and translocation within the plant are presumed to occur via a phosphate/proton cotransport mechanism. PRINCIPAL FINDINGS: We cloned two cDNAs from soybean (Glycine max), GmPT1 and GmPT2, which show homology to the phosphate/proton cotransporter PHO84 from the budding yeast Saccharomyces cerevisiae. The amino acid sequence of the products predicted from GmPT1 and GmPT2 share 61% and 63% identity, respectively, with the PHO84 in amino acid sequence. The deduced structure of the encoded proteins revealed 12 membrane-spanning domains with a central hydrophilic region. The molecular mass values are ∼58.7 kDa for GmPT1 and ∼58.6 kDa for GmPT2. Transiently expressed GFP-protein fusions provide direct evidence that the two Pi transporters are located in the plasma membrane. Uptake of radioactive orthophosphate by the yeast mutant MB192 showed that GmPT1 and GmPT2 are dependent on pH and uptake is reduced by the addition of uncouplers of oxidative phosphorylation. The K(m) for phosphate uptake by GmPT1 and GmPT2 is 6.65 mM and 6.63 mM, respectively. A quantitative real time RT-PCR assay indicated that these two genes are expressed in the roots and shoots of seedlings whether they are phosphate-deficient or not. Deficiency of phosphorus caused a slight change of the expression levels of GmPT1 and GmPT2. CONCLUSIONS: The results of our experiments show that the two phosphate transporters have low affinity and the corresponding genes are constitutively expressed. Thereby, the two phosphate transporters can perform translocation of phosphate within the plant

Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust

Learning semantic parsers using statistical syntactic parsing techniques

Most recent work on semantic analysis of natural language has focused on “shallow ” semantics such as word-sense disambiguation and semantic role labeling. Our work addresses a more ambitious task we call semantic parsing where natural language sentences are mapped to complete formal meaning representations. We present our system SCISSOR based on a statistical parser that generates a semanticallyaugmented parse tree (SAPT), in which each internal node has both a syntactic and semantic label. A compositional-semantics procedure is then used to map the augmented parse tree into a final meaning representation. Training the system requires sentences annotated with augmented parse trees. We evaluate the system in two domains, a natural-language database interface and an interpreter for coaching instructions in robotic soccer. We present experimental results demonstrating that SCISSOR produces more accurate semantic representations than several previous approaches on long sentences. In the future, we intend to pursue several directions in developing more accurate semantic parsing algorithms and automating the annotation process. This work will involve exploring alternative tree representations for better generalization in parsing. We also plan to apply discriminative reranking methods to semantic parsing, which allows exploring arbitrary, potentially correlated features not usable by th

Learning a Compositional Semantic Parser using an Existing Syntactic Parser

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.

Danshen-Honghua Ameliorates Stress-Induced Menopausal Depression in Rats

Objective. Previously, we have shown that Danshen-Honghua (DSHH) for cognitive deficits after ischemia induced impairments of the hippocampus. Here, we investigate the effects of DSHH on stress-induced depression in menopausal rats. Methods. A rat model with menopausal depression was established with bilateral ovariectomies in female SD rats followed by chronic mild stress treatment for 21 days. 40 rats were randomly divided into the sham surgery group (sham surgery and no stress treatment), surgery group (surgery with no stress treatment), surgery/stress group (surgery and stress treatment), fluoxetine group (2.4 mg·kg−1, with surgery and stress treatment), and DSHH group (35 g·kg−1, with surgery and stress treatment). The rats in the last two groups were treated with stresses together with intragastric drug administration for three weeks after the surgery. Then open-field locomotor scores and sucrose intake were tested for behavior changes. Also, the levels of norepinephrine (NE), dopamine (DA), serotonin (5-HT), and cortisone were determined by high-performance liquid chromatography (HPLC). Serum estradiol (E2), follicle-stimulating hormone (FSH), and luteinizing hormone (LH) were determined by radioimmunoassay. Results. The results of open-field locomotor scores, sucrose intake in both the fluoxetine group and DSHH group, were significantly higher than those of the surgery/stress group (P<0.01). Serum LH, FSH, and cortisone levels in both the DSHH group and fluoxetine group were significantly lower than those in the surgery/stress group (P<0.01). Serum E2 levels in these groups were slightly increased in these medicine groups (P<0.01). The monoamine levels in the DSHH group were much higher than those in the surgery/stress group (P<0.01). Conclusion. DSHH can ameliorate stress-induced depressed syndromes in the surgery/stressed rats via regulating LH and FSH levels as well as monoamine levels

Synthesis of Magnetite–Semiconductor–Metal Trimer Nanoparticles through Functional Modular Assembly: A Magnetically Separable Photocatalyst with Photothermic Enhancement for Water Reduction

Hybrid nanoparticles have intrinsic advantages to achieve better activity in photocatalysis compared to single-component materials, as it can synergistically combine functional components, which promote light absorption, charge transportation, surface reaction, and catalyst regeneration. Through functional modular assembly, a rational and stepwise approach has been developed to construct Fe₃O₄–CdS–Au trimer nanoparticles and its derivatives as magnetically separable catalysts for photothermo-catalytic hydrogen evolution from water. In a typical step-by-step synthetic process, Fe₃O₄–Ag dimers, Fe₃O₄–Ag₂S dimers, Fe₃O₄–CdS dimers, and Fe₃O₄–CdS–Au trimers were synthesized by seeding growth, sulfuration, ion exchange, and in situ reduction consequently. Following the same reaction route, a series of derivative trimer nanoparticles with alternative semiconductor and metal were obtained for water-reduction reaction. The experimental results show that the semiconductor acts as an active component for photocatalysis, the metal nanoparticle acts as a cocatalyst for enhancement of charge separation, and the Fe₃O₄ component helps in the convenient separation of catalysts in magnetic field and improves photocatalytic activity under near-infrared illumination due to photothermic effect

Learning Transformation Rules for Semantic Parsing

This paper presents an approach for inducing transformation rules that map natural-language sentences into a formal semantic representation language. The approach assumes a formal grammar for the target representation language and learns transformation rules that exploit the non-terminal symbols in this grammar. Patterns for the transformation rules are learned using an induction algorithm based on longestcommon-subsequences previously developed for an information extraction system. Experimental results are presented on learning to map English coaching instructions for Robocup soccer into an existing formal language for coaching simulated robotic agents.

Risk Analysis of Instability Failure of Earth–Rock Dams Based on the Fuzzy Set Theory

Determining the anti-sliding instability risk of earth–rock dams involves the analysis of complex uncertain factors, which are mostly regarded as random variables in traditional analysis methods. In fact, fuzziness and randomness are two inseparable uncertainty factors influencing the stability of earth–rock dams. Most previous research only focused on the randomness or the fuzziness of individual variables. Moreover, dam systems present a fuzzy transition from a stable state into a failure state. Therefore, both fuzziness and randomness of the influencing factors should be considered in the same framework, where the instability of an earth–rock dam is regarded as a mixed process. In this paper, a fuzzy risk model of instability of earth–rock dams is established by considering the randomness and fuzziness of parameters and the failure criteria comprehensively. We obtained the probability threshold of instability risk of earth–rock dams by Monte-Carlo simulation after the fuzzy parameters were transformed into interval numbers by cut set levels. By applying the proposed model to the instability analysis of the Longxingsi Reservoir, the calculation results showed that the lower limits of risk probability under different cut set levels exceeded the instability risk standard of grade C for earth–rock dams. Compared with the traditional risk determination value, the risk interval obtained with the proposed methods reflects different degrees of dam instability risk and can provide reference for dam structure safety assessment and management