Architecture for text normalization using statistical machine translation techniques

This paper proposes an architecture, based on statistical machine translation, for developing the text normalization module of a text to speech conversion system. The main target is to generate a language independent text normalization module, based on data and flexible enough to deal with all situa-tions presented in this task. The proposed architecture is composed by three main modules: a tokenizer module for splitting the text input into a token graph (tokenization), a phrase-based translation module (token translation) and a post-processing module for removing some tokens. This paper presents initial exper-iments for numbers and abbreviations. The very good results obtained validate the proposed architecture.

What Makes an Effective Abstract in Sport Science?

Writing an efficient abstract is always a difficult and significant work in academic writing. What kinds of abstracts are well reputed in sport science? To answer this question, 20 abstracts from top journals of sport science were analyzed in the current research. The number of words and rhetorical moves were studied to assess the structures of the abstracts. Meanwhile, the key clauses, citations, the use of first person pronoun, the adoption of abbreviations and acronyms, hedging and the main tense were included in the analysis of the writing skills. Results have show: (1) Almost all of the abstracts were non-structured, and the length varied a lot, but the average word count was about 210-220; (2) the use of writing skills, such as key clauses, citations and hedging differed depending on the preference of the journal where the abstract appeared, and the main tense was selected based on the context of the abstract. In most cases, abbreviations and acronyms were allowed to be used, while the first person pronoun was always avoided