shi Sato

Vapor-phase beckmann rearrangement over alumina-supported boria catalyst prepared by vapor decomposition method

Abstract The present work introduced a new method of catalyst preparation which involved the vapor decomposition of B(OEt) 3 onto silica gel to produce a silica-supported boria ( B 2 O 3 SiO 2 ). A B 2 O 3 SiO 2 catalyst prepared by this method (B 2 O 3 content 34 wt%) showed high catalytic efficiency for the vapor-phase Beckmann rearrangement of cyclohexanone oxime (oxime conversion: 98%, e-caprolactam selectivity: 96 mol%, at 250 °C). The vapor decomposition B 2 O 3 SiO 2 was more active and selective at any B 2 O 3 content than the B 2 O 3 SiO 2 obtained by the ordinary impregnation method using H 3 BO 3 . The amounts of the acid sites whose acid strengths exceeded 80 kJ/mol in terms of the differential heat of adsorption of ammonia (DHA) were 0.7 and 0.4 mmol/g for the most active vapor decomposition B 2 O 3 SiO 2 and the most active impregnation B 2 O 3 SiO 2 , respectively. The vapor decomposition effected uniform deposition of B 2 O 3 on SiO 2 , and produced a solid acid with relatively uniform distribution of acid strength. It is suggested that the acid strength ( H 0 ) of an effective catalyst pertinent to the vapor-phase Beckmann rearrangement should be less than −5.6 which approximately corresponds to a DHA value of more than 80 kJ/mol.

Compilation of a dictionary of japanese functional expressions with hierarchical organization

The Japanese language has a lot of functional expressions, which consist of more than one word and behave like a single functional word. A remarkable characteristic of Japanese functional expressions is that each functional expression has many different surface forms. This paper proposes a methodology for compilation of a dictionary of Japanese functional expressions with hierarchical organization. We use a hierarchy with nine abstraction levels: the root node is a dummy node that governs all entries; a node in the first level is a headword in the dictionary; a leaf node corresponds to a surface form of a functional expression. Two or more lists of functional expressions can be integrated into this hierarchy. This hierarchy also provides a way of systematic generation of all different surface forms. We have compiled the dictionary with 292 headwords and 13,958 surface forms, which covers almost all of major functional expressions.

A Compositional Approach toward Dynamic Phrasal Thesaurus

To enhance the technology for computing semantic equivalence, we introduce the notion of phrasal thesaurus which is a natural extension of conventional word-based the saurus. Among a variety of phrases that conveys the same meaning, i.e., paraphrases, we focus on syntactic variants that are compositionally explainable using a small number of atomic knowledge, and develop a system which dynamically generates such variants. This paper describes the proposed system and three sorts of knowledge developed for dynamic phrasal thesaurus in Japanese: (i) transformation pattern, (ii) generation function, and (iii) lexical function.

Chunking Japanese Compound Functional Expressions by Machine Learning

The Japanese language has various types of compound functional expressions, which are very important for recognizing the syntactic structures of Japanese sentences and for understanding their semantic contents. In this paper, we formalize the task of identifying Japanese compound functional expressions in a text as a chunking problem. We apply a machine learning technique to this task, where we employ that of Support Vector Machines (SVMs). We show that the proposed method significantly outperforms existing Japanese text processing tools.

Collecting novel technical terms from the web by estimating domain specificity of a term

This paper proposes a method of domain specificity estimation of technical terms using the Web. In the proposed method, it is assumed that, for a certain technical domain, a list of known technical terms of the domain is given. Technical documents of the domain are collected through the Web search engine, which are then used for generating a vector space model for the domain. The domain specificity of a target term is estimated according to the distribution of the domain of the sample pages of the target term. We apply this technique of estimating domain specificity of a term to the task of discovering novel technical terms that are not included in any of existing lexicons of technical terms of the domain. Out of randomly selected 1,000 candidates of technical terms per a domain, we discovered about 100 ~ 200 novel technical terms.

A comparative study on compositional translation estimation using a domain/topic-specific corpus collected from the web

This paper studies issues related to the compilation of a bilingual lexicon for technical terms. In the task of estimating bilingual term correspondences of technical terms, it is usually rather difficult to find an existing corpus for the domain of such technical terms. In this paper, we adopt an approach of collecting a corpus for the domain of such technical terms from the Web. As a method of translation estimation for technical terms, we employ a compositional translation estimation technique. This paper focuses on quantitatively comparing variations of the components in the scoring functions of compositional translation estimation. Through experimental evaluation, we show that the domain/topic-specific corpus contributes toward improving the performance of the compositional translation estimation.

A Probabilistic Model for Measuring Grammaticality and Similarity of Automatically Generated Paraphrases of Predicate Phrases

The most critical issue in generating and recognizing paraphrases is development of wide-coverage paraphrase knowledge. Previous work on paraphrase acquisition has collected lexicalized pairs of expressions; however, the results do not ensure full coverage of the various paraphrase phenomena. This paper focuses on productive paraphrases realized by general transformation patterns, and addresses the issues in generating instances of phrasal paraphrases with those patterns. Our probabilistic model computes how two phrases are likely to be correct paraphrases. The model consists of two components: (i) a structured N-gram language model that ensures grammaticality and (ii) a distributional similarity measure for estimating semantic equivalence and substitutability.

Detecting Japanese idioms with a linguistically rich dictionary

Detecting idioms in a sentence is important to sentence understanding. This paper discusses the linguistic knowledge for idiom detection. The challenges are that idioms can be ambiguous between literal and idiomatic meanings, and that they can be “transformed” when expressed in a sentence. However, there has been little research on Japanese idiom detection with its ambiguity and transformations taken into account. We propose a set of linguistic knowledge for idiom detection that is implemented in an idiom dictionary. We evaluated the linguistic knowledge by measuring the performance of an idiom detector that exploits the dictionary. As a result, more than 90% of the idioms are detected with 90% accuracy.

Web-Based Transliteration of Person Names

We have developed a web-based transliteration system of person names; from a person name written in English (Latin script), the system produces its Japanese (Katakana) transliteration extracted from the Web. Experiments have shown that the performance is sufficiently high: for 89.4% of English person names, the system produced one or more acceptable Japanese transliterations; 98.5% of system’s outputs were acceptable transliterations. This system was used for automatic compilation of an English-Japanese person-name lexicon with 406K entries.

Learning Dependency Relations of Japanese Compound Functional Expressions

This paper proposes an approach of processing Japanese compound functional expressions by identifying them and analyzing their dependency relations through a machine learning technique. First, we formalize the task of identifying Japanese compound functional expressions in a text as a machine learning based chunking problem. Next, against the results of identifying compound functional expressions, we apply the method of dependency analysis based on the cascaded chunking model. The results of experimental evaluation show that, the dependency analysis model achieves improvements when applied after identifying compound functional expressions, compared with the case where it is applied without identifying compound functional expressions.