This paper aims to reveal the rhetorical structure and the linguistic features of persuasive language in online advertisements of electronic products. Nowadays, the bulk of e-commerce is carried out in English, and it is often the case that non-native speakers are required to write different text types for various professional purposes, including promotional texts. This need has prompted the present study and the results have been used to build software to help native speakers of Spanish when writing promotional texts in English. The analysis reveals that these texts typically have two main rhetorical moves: one for identifying the product and another one for describing it. The latter move is further divided into two steps: one including objective features (size, weight, etc.) and the other focusing on persuading the potential customer. This is mainly achieved with the use of a relatively informal style (imperatives, contractions, clipping, subject/auxiliary omissions, etc.) and lexico-grammatical elements conveying positive evaluation (multiple modification, multal quantifying expressions, etc.). The findings show that online advertisements of electronic products may be regarded as a specific subgenre with particular macro-and microlinguistic characteristics, which have been identified in this paper for technical writing assistance.

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Nowadays, there are many options for corpus linguistic analysis that make use of different approaches for corpus storage. There are tools based on SQL databases, dedicated implementations such as CQP/CWB and others that employ plain-text corpora. NoSQL databases have been widely used for big data, data mining and even sentiment analysis. However, as far as we can see, there is a lack of a widespread concordancer or consolidated framework that makes use of MongoDB architecture for the purposes of corpus linguistics. This paper aims to describe the architecture of a software that allows users to analyse monolingual and bilingual parallel corpora with grammatical annotation using MongoDB technology. Our premises are that MongoDB is ideal for non-structured data and provides high flexibility and scalability, so it may be also useful for corpus linguistic research. We analyse functionalities of MongoDB such as text search indexes and query format in order to examine its suitability.

The technologization of cross-linguistic communication and the expansion of the learning of foreign languages has helped create new, non-linguist users. Corpus-based applications offer a way of responding to these new challenges. This presentation focuses on the design and building process of the BiTeX app, designed to write recipes in both En and Es through controlled language choices. The starting point is a custom-made, rhetorically and POS annotated, En-Es comparable corpus of recipes containing 135,912 words in the En and 145,449 in the Es subcorpus respectively. The BiText prototype has been developed using MongoDB 1 , Express 2 , Node.js 3 and jQuery 4 , which allow for multiple concurrent connections to be handled without I/O blocks. BiTeX aims at helping improve international communication in the restaurant and catering community, as well as boosting collateral business niches in including recipe books, tourist-oriented websites, etc.

This paper explores the multi-layer annotation of a written domain-restricted English-Spanish comparable corpus (CLANES – Controlled LANguage English Spanish), focusing on pragmatic annotation. The annotation scheme draws on part of speech tagging and a semantic annotation scheme, i.e. the UCREL Semantic Analysis System, with some added categories to fit the food-and-drink domain represented in CLANES. These are used to build significant (pragmatic) metapatterns. Seven different pragmatic functions have been identified in our corpus, namely <STATE>, <DIRECT>, <SUGGEST>, <RECOMMEND>, <PRAISE>, <EVIDENCE> and <RELATE TO READER>. Computer scripts translate this linguistic information into regular expressions to be used in unsupervised annotation. Partial results indicate that applying lexical restrictors boosts the success rate considerably. However, metadata is preferred because of increased replicability and generality. Replicability issues and limitations encountered during testing are also addressed.