yet another meta-language for programming language processing

The formal specification language LFC was designed to support formal specification acquisition. However, it is yet suited to be used as a meta-language for specifying programming language processing. This paper introduces LFC as a meta-language, and compares it with ASF+SDF, an algebraic specification formalism that can also be used to programming languages.

combining cfg and recursive functions to get a new language

We propose a new functional programming language(FPL) which differs in some aspects from most well known FPLs[l].We descrihc the prohlmm domain,the language,explain why we need it.

smoothing document language model with local word graph

ACM SIGIR; ACM SIGWEB

Effects of multiple environmental factors on CO2 emission and CH4 uptake from old-growth forest soils

National Key Research and Development Program [2010CB833502]; National Natural Science Foundation of China [30600071, 40601097, 30590381, 30721140307]; Knowledge Innovation Project of the Chinese Academy of Sciences [KZCX2-YW-432, O7V70080SZ, LENOM07LS-01

汉语婴儿对早期词汇的理解与表达

Whether Mandarin is a verb-friendly language for young word learners or not is a hotly debated issue. Researches on children's word learning were either naturalistic study or experimental study, and they were from cross-cultural perspective. This study tries to examine the noun/verb proportion in Mandarin infants, from a longitudinal perspective; it also examines how Mandarin infants understand their first nouns and verbs using both naturalistic study and experimental study. The results of this research are: 1） According to the results of CDI test, Mandarin infants of 18 month old could say more verbs than nouns significantly; while 24 month-olds could say more nouns than verbs significantly. 2） According to the results of CDI test, Mandarin infants’ verb/noun proportion was higher in 18 month old than in 24 month old. This means that the relative advantage of verb learning may be easier to show up in infants younger than 2 years old. 3） The total vocabulary of 18 month-olds was positively correlated with the Mean Length of Utterances(MLU) of 24 month-olds. The MLU of 24 month olds was positively correlated with the verbs of 18 month-olds. 4） Only 24 month-old could succeed in our IPLP study and showed “true” understanding to their familiar words. 14- and 18- month-olds watch the target side and non-target side randomly. 5） According to the results of IPLP study, the infants could understand nouns better than verbs. Both female and male infants understood nouns better than verbs, but females seemed to have stronger tendency to watch the object-same side in both noun and verb condition. 6） According to the results of parents’ reports, all the infants in 3 age groups could understand verbs as well as nouns, and they are quite familiar with those words. However, IPLP study results showed that these infants understood nouns better than verbs. 7） In IPLP study, less than 50% of the Mandarin infants watch the target side of verbs longer than the non-target side (action-same response). However, the AS responses appear more frequently in verb condition than in noun condition among 24-month-olds.

Natural Language Syntax and First Order Preference

We have argued elsewhere that first order inference can be made more efficient by using non-standard syntax for first order logic. In this paper we show how a fragment of English syntax under Montague semantics provides the foundation of a new inference procedure. This procedure seems more effective than corresponding procedures based on either classical syntax of our previously proposed taxonomic syntax. This observation may provide a functional explanation for some of the syntactic structure of English.

Computational Consequences of Agreement and Ambiguity in Natural Language

The computer science technique of computational complexity analysis can provide powerful insights into the algorithm-neutral analysis of information processing tasks. Here we show that a simple, theory-neutral linguistic model of syntactic agreement and ambiguity demonstrates that natural language parsing may be computationally intractable. Significantly, we show that it may be syntactic features rather than rules that can cause this difficulty. Informally, human languages and the computationally intractable Satisfiability (SAT) problem share two costly computional mechanisms: both enforce agreement among symbols across unbounded distances (Subject-Verb agreement) and both allow ambiguity (is a word a Noun or a Verb?).