TermEnsembler

This paper describes TermEnsembler, a bilingual term extraction and alignment system utilizing a novel ensemble learning approach to bilingual term alignment. In the proposed system, the processing starts with monolingual term extraction from a language industry standard file type containing aligned English and Slovenian texts. The two separate term lists are then automatically aligned using an ensemble of seven bilingual alignment methods, which are first executed separately and then merged using the weights learned with an evolutionary algorithm. In the experiments, the weights were learned on one domain and tested on two other domains. When evaluated on the top 400 aligned term pairs, the precision of term alignment is over 96%, while the number of correctly aligned multi-word unit terms exceeds 30% when evaluated on the top 400 term pairs.

Emoticons in informal text communication: a new window on bilingual alignment

The study of emoticon use in text communication is in its early stages (Aragon, Feldman, Chen & Kroll, 2014), with even less known about how emoticons function in multilingual environments. We describe a preliminary longitudinal analysis of text communication in an online bilingual scientific work environment and demonstrate how patterns of emoticon use constitute a novel yet systematic nonverbal aspect of communication. Specifically, coordination over bilingual speakers entails reductions in emoticon diversity over time that are greater for those who communicate in their L2 than in their L1. An analogous but weaker pattern is evident for lexical diversity in L2 but not L1. We hypothesize that reductions in emoticon diversity in the L2 are likely to reflect social contributions to alignment rather than purely proficiency.

An Instance of Constructing Conceptual Model of Statistical Machine Translation for Processing Natural Language

The basic frame of Example based machine translation is concerned in this paper. Some key issues, such as bilingual alignment, similarity measure between input sentence and example, and template acquisition, are introduced.