Lecture Organic chemistry - Chapter 7: Unimolecular substitution and elimination. The main contents of this chapter include all of the following: Some observations, a new mechanism, what is the intermediate? The mechanism explains the data, the strong effect of polar solvents on the SN1 reaction,...and other contents.
We propose a model that incorporates an insertion operator in Bayesian tree substitution grammars (BTSG). Tree insertion is helpful for modeling syntax patterns accurately with fewer grammar rules than BTSG. The experimental parsing results show that our model outperforms a standard PCFG and BTSG for a small dataset. For a large dataset, our model obtains comparable results to BTSG, making the number of grammar rules much smaller than with BTSG.
Chapter 6 - Nucleophilic substitution of haloalkanes. This chapter presents the following content: Haloalkanes, the C-X bond is polarized, physical properties of R-X, nucleophilic substitution: general, remember acid-base reaction, large number of reactions,...
We describe our experiments with training algorithms for tree-to-tree synchronous tree-substitution grammar (STSG) for monolingual translation tasks such as sentence compression and paraphrasing. These translation tasks are characterized by the relative ability to commit to parallel parse trees and availability of word alignments, yet the unavailability of large-scale data, calling for a Bayesian tree-to-tree formalism.
Learning a tree substitution grammar is very challenging due to derivational ambiguity. Our recent approach used a Bayesian non-parametric model to induce good derivations from treebanked input (Cohn et al., 2009), biasing towards small grammars composed of small generalisable productions. In this paper we present a novel training method for the model using a blocked Metropolis-Hastings sampler in place of the previous method’s local Gibbs sampler.
Most text message normalization approaches are based on supervised learning and rely on human labeled training data. In addition, the nonstandard words are often categorized into different types and speciﬁc models are designed to tackle each type. In this paper, we propose a uniﬁed letter transformation approach that requires neither pre-categorization nor human supervision.
In this paper, we show that local features computed from the derivations of tree substitution grammars — such as the identify of particular fragments, and a count of large and small fragments — are useful in binary grammatical classiﬁcation tasks. Such features outperform n-gram features and various model scores by a wide margin. Although they fall short of the performance of the hand-crafted feature set of Charniak and Johnson (2005) developed for parse tree reranking, they do so with an order of magnitude fewer features. ...
We present an approach of expanding parallel corpora for machine translation. By utilizing Semantic role labeling (SRL) on one side of the language pair, we extract SRL substitution rules from existing parallel corpus. The rules are then used for generating new sentence pairs. An SVM classiﬁer is built to ﬁlter the generated sentence pairs. The ﬁltered corpus is used for training phrase-based translation models, which can be used directly in translation tasks or combined with baseline models. ...
Processing discourse connectives is important for tasks such as discourse parsing and generation. For these tasks, it is useful to know which connectives can signal the same coherence relations. This paper presents experiments into modelling the substitutability of discourse connectives. It shows that substitutability effects distributional similarity. A novel variancebased function for comparing probability distributions is found to assist in predicting substitutability.
This paper deals with the task of ﬁnding generally applicable substitutions for a given input term. We show that the output of a distributional similarity system baseline can be ﬁltered to obtain terms that are not simply similar but frequently substitutable. Our ﬁlter relies on the fact that when two terms are in a common entailment relation, it should be possible to substitute one for the other in their most frequent surface contexts.
A series of seven azomethines has been synthesized from p-nitrobenzaldehyde and different substituted anilines. The structures of products have been determined by the data of IR-spectra. All obtained azomethines have metallic inhibition corrosion capacities on CT-3 steel and aluminum in different levels: the protection effects were 41-57% on CT-3 steel and 83-92% on aluminum. The influences of structures to their metallic inhibition corrosion capacities have been discussed.
We propose Symbol-Reﬁned Tree Substitution Grammars (SR-TSGs) for syntactic parsing. An SR-TSG is an extension of the conventional TSG model where each nonterminal symbol can be reﬁned (subcategorized) to ﬁt the training data. We aim to provide a uniﬁed model where TSG rules and symbol reﬁnement are learned from training data in a fully automatic and consistent fashion.
We investigate the potential of Tree Substitution Grammars as a source of features for native language detection, the task of inferring an author’s native language from text in a different language. We compare two state of the art methods for Tree Substitution Grammar induction and show that features from both methods outperform previous state of the art results at native language detection.
Letter-substitution ciphers encode a document from a known or hypothesized language into an unknown writing system or an unknown encoding of a known writing system. It is a problem that can occur in a number of practical applications, such as in the problem of determining the encodings of electronic documents in which the language is known, but the encoding standard is not. It has also been used in relation to OCR applications. In this paper, we introduce an exact method for deciphering messages using a generalization of the Viterbi algorithm. ...
Tree substitution grammars (TSGs) offer many advantages over context-free grammars (CFGs), but are hard to learn. Past approaches have resorted to heuristics. In this paper, we learn a TSG using Gibbs sampling with a nonparametric prior to control subtree size. The learned grammars perform signiﬁcantly better than heuristically extracted ones on parsing accuracy.
Th=s report describes Paul, a computer text generation system desig~ed LO create cohesive text through the use o| lexlcal substitutions. Specihcally, Ihas system is designed Io determmistically choose between provluminahzat0on, superordinate suhstntut0on, and dehmte noun phrase reiterabon. The system identities a strength el antecedence recovery for each of the lex~cal subshtutions, and matches them against the strength el potenfml antecedence of each element m the text to select the proper substitutions for these elements. ...
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Evaluation of a recombinant human gelatin as a substitute for a hydrolyzed porcine gelatin in a refrigerator-stable Oka/Merck live varicella vaccine...
More precisely, the question to be addressed is whether there are substitutes available
at less cost than the true cost of Chemical A. Substitutes that meet this requirement will not
always be available. For instance, "retro-fitting" existing production facilities in order to
employ substitutes is, as a rule, likely to be quite expensive. The availability of financially
viable alternative actions may be higher at the design stage of a process or product's
Shell Programming have objectives: What is a Shell Program, common Shells, concepts of shell programming, how shell programs are executed, concepts and use of shell variables, how command line arguments are passed shell programs, concepts of command substitution, basic coding principles, write and discuss shell scripts.