N‐(
            <i>p</i>
            ‐Acetylaminophenyl)rhodanine

Strube, R. E.

doi:10.1002/0471264180.os039.01

Cited by 3 publications

(7 citation statements)

References 1 publication

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“…As we can see, both ESA techniques yield substantial improvements over previous state of the art results. Notably, ESA also achieves much better results than another recently introduce method based on Wikipedia (Strube & Ponzetto, 2006). We provide a detailed comparison of our approach with this latter work in Section 5.1.…”

Section: Resultsmentioning

confidence: 80%

“…In the WikiRelate! paper (Strube & Ponzetto, 2006), the authors report results of as many as 6 different method variations, and again we report the performance of the best one (based on the metric proposed by Leacock and Chodorow, 1998).…”

Section: Resultsmentioning

confidence: 99%

“…Recently, Hughes and Ramage (2007) proposed a method for computing semantic relatedness using random graph walks; their results on the WordSimilarity-353 dataset are competitive with those reported by Jarmasz (2003) and Finkelstein et al (2002a). Strube and Ponzetto (2006) proposed an alternative approach to computing word similarity based on Wikipedia, by comparing articles in whose titles the words occur. We discuss this approach in greater detail in Section 5.1.…”

Section: Prior Workmentioning

confidence: 99%

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Wikipedia-based Semantic Interpretation for Natural Language Processing

Gabrilovich¹,

Markovitch²

2009

jair

312

274

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Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users

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Section: Resultsmentioning

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

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Wikipedia-based Semantic Interpretation for Natural Language Processing

Gabrilovich¹,

Markovitch²

2009

jair

312

274

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“…Strube prepared a new derivative of rhodanine, namely N-( p-acetylaminophenyl)rhodanine by the Holmberg procedure in aqueous solution (Scheme 9.6). This method begins with p-aminoacetanilide and trithiocarbodiglycolic acid in the presence of water as a solvent under heating conditions to give target molecule 49-53 % yield [19]. A series of 2-(5-((naphthalen-6-yl)methylene)-4-oxo-2-thioxothiazolidin-3-yl) acetic acid analogues have been prepared as aldose reductase inhibitors.…”

Section: Synthesis Of Rhodanine-based Scaffoldsmentioning

confidence: 99%

Synthesis and Synthetic Applications of Biologically Interesting Rhodanine and Rhodanine-Based Scaffolds

Rostamnia

Doustkhah

2014

Green Chemistry: Synthesis of Bioactive Heterocycles

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Rhodanine and thiazolidinone (TZD) heterocycles are attractive targets in organic and medicinal chemistry owing to their potency in a wide spectrum of biological activities and can also serve as synthetic intermediates for many kinds of pharmaceuticals or drug precursors. Consequently, looking for efficient and concise green methods for the synthesis of these types of compounds is a major challenge in chemistry. As a result, many green methods have been used to synthesize structurally complex and diverse rhodanine and TZDs in recent years. The purpose of this chapter is to discuss the recent green synthesis of the rhodanine and TZD heterocyclic scaffolds including aqueous medium synthesis, ionic liquid, microwave, ultrasonic irradiation, solvent-free methods, solid catalysts, such as mesoporous, magnetic nanoparticles, etc.

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“…Path-based semantic distances such as the one above were shown to be successful in many NLP applications. For example, the semantic relatedness of two words was measured by means of a function that measured the distance between words in a taxonomy (Rada, Mili, Bicknell, & Blettner, 1989;Strube & Ponzetto, 2006). We build on this metric and expand it to handle events that are structured and can contain several objects from different ontologies.…”

Section: Generalizing Over Objects and Actionsmentioning

confidence: 99%

Learning to Predict from Textual Data

Radinsky¹,

Davidovich²,

Markovitch³

2012

jair

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Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mine 150 years of news articles and apply semantic natural language modeling techniques to headlines containing certain predefined causality patterns. For generalization, the model uses a vast number of world knowledge ontologies. Empirical evaluation on real news articles shows that our Pundit algorithm performs as well as non-expert humans

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N‐( p ‐Acetylaminophenyl)rhodanine

Abstract: N‐( p ‐acetylaminophenyl)rhodanine product: N‐( p ‐acetylaminophenyl)rhodanine

Cited by 3 publications

References 1 publication

Wikipedia-based Semantic Interpretation for Natural Language Processing

Wikipedia-based Semantic Interpretation for Natural Language Processing

Synthesis and Synthetic Applications of Biologically Interesting Rhodanine and Rhodanine-Based Scaffolds

Learning to Predict from Textual Data

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