Visualisation of collocational preferences for near-synonym discrimination

This paper aims to explore the potential usefulness of two techniques that visualise collocational preference for the purpose of synonym discrimination. Given the fact that collocation is one of the most important markers of meaning difference, it is used as the criterion for distin-guishing between near-synonyms. Collocational preferences for a set of near-synonyms (artificial, fake, false, and synthetic) were visualised using two techniques: correspondence analysis plot and collocational network. The collocations were retrieved from BNC corpus by using a distributional method. An advantage of the graphs is that they allow lexicographers to spot similarities and dif-ferences in collocational preference of several words in a single diagram. Such visualisations may be used as an alternative way to a tabular form of data presentation to avoid information overload which arises when lexicographers prepare synonym essays for productively-oriented dictionaries. The visualisations can be used as a starting point for exploring semantic differences between semantically similar words.Keywords: Visualisation, Correspondence Analysis, Collocational Net-Work, Synonymy Discrimination, Collocation, Automatic Retrieval Of Syno-Nyms, Near-Synonym, Collocation Preferences

Lexical Co-occurrence: The Missing Link

Aside from syntax, linguistic knowledge can be separated into two distinct parts, encyclopedic knowledge and dictionary knowledge. Encyclopedic knowledge describes the world whereas the dictionary describes individual word features, thus capturing lexical knowledge. Among the various types of lexical knowledge, one has generally been overlooked and should bring new results in computational linguistics: co-occurrence knowledge. Co-occurrence knowledge stands for the extent to which an item is specified by its environment independently of syntactic or semantic reasons. The basic concept is that of a lexical relation due to Saussure [49]. A lexical relation between two units of language stands for a correlation of common appearance of the two units in the utterances of the language

Dictionaries for Language Generation Accounting for Co-occurrence Knowledge

Many wording choices in English sentences cannot be accounted for on semantic or syntactic grounds. They can only be expressed in terms of co-occurrence relations. Co-occurrence knowledge has been traditionally overlooked in the past, but should be included in lexicons as it is an inherent part of the language. In this paper, we demonstrate the importance of co-occurrence knowledge for language generation and we show how to include it in computational dictionaries. Using co-occurrence knowledge in the dictionary provides the generator with the information necessary for handling many lexical decisions that were previously ignored. We focus here on the process of building the dictionary, and we show how co-occurrence knowledge can be systematically entered in lexicons. Lexical relations are first identified by a co-occurrence compiler, EXTRACT. Then, domain specific semantic information is used as a criterion for classifying them. We exemplify our approach in the banking domain and we explain how it can be used by a natural language generator

The Conceptual Outline of Perception in Terms of Greimassian Semiotic Theory

The current study addresses the nature of the relation between perceptual, cognitive, emotional processes, and linguistic experience within the observer framework. As an extension of the already conducted investigations into the semiotics of the sensible, the conceptual triad SENSE : FEELING : EMOTION is introduced. The study argues that the ternion in question is a component of human expressivity constituting the semiotic space of the thymic category. The synergism of cognitive-semantic characteristics of the three concepts under study corresponds to the basic level of categorisation in modern English. More significantly, the aim of the analysis is to consider the valency models of the names of the conceptual triad SENSE : FEELING : EMOTION as well as their collocability potential. The present paper highlights that the structures of predicate valency of conceptual dependencies FEELING → SENSE (the valency index equals 0,38) outnumber the analogous structures FEELING → EMOTION (the valency index equals 0,24). The structures of object valency of the combinations FEELING → EMOTION (the valency index is 0,9) prevail over the combinations SENSE → FEELING (with the valency index of 0,1). The data obtained confirm the idea that the correlation between observer and observable is twofold: cognitive-perceptual correlation denotes the observer’s outward perspective, whereas cognitive-emotional correlation denotes the observer’s inward perspective

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

Big data warehouse framework for smart revenue management

Revenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Managemen