Mei-Hua Chen

Bilingual Keyword Extraction and its Educational Application

We introduce a method that extracts keywords in a language with the help of the other. The method involves estimating preferences for topical keywords and fusing language-specific word statistics. At run-time, we transform parallel articles into word graphs, build crosslingual edges for word statistics integration, and exploit PageRank with word keyness information for keyword extraction. We apply our method to keyword analysis and language learning. Evaluation shows that keyword extraction benefits from cross-language information and language learners benefit from our keywords in reading comprehension test.

GLANCE Visualizes Lexical Phenomena for Language Learning

Facilitating vocabulary knowledge is a challenging aspect for language learners. Although current corpus-based reference tools provide authentic contextual clues, the plain text format is not conducive to fully illustrating some lexical phenomena. Thus, this paper proposes GLANCE 1 , a text visualization tool, to present a large amount of lexical phenomena using charts and graphs, aimed at helping language learners understand a word quickly and intuitively. To evaluate the effectiveness of the system, we designed interfaces to allow comparison between text and graphics presentation, and conducted a preliminary user study with ESL students. The results show that the visualized display is of greater benefit to the understanding of word characteristics than textual display.

A Computer-Assisted Translation and Writing System

We introduce a method for learning to predict text and grammatical construction in a computer-assisted translation and writing framework. In our approach, predictions are offered on the fly to help the user make appropriate lexical and grammar choices during the translation of a source text, thus improving translation quality and productivity. The method involves automatically generating general-to-specific word usage summaries (i.e., writing suggestion module), and automatically learning high-confidence word- or phrase-level translation equivalents (i.e., translation suggestion module). At runtime, the source text and its translation prefix entered by the user are broken down into n-grams to generate grammar and translation predictions, which are further combined and ranked via translation and language models. These ranked prediction candidates are iteratively and interactively displayed to the user in a pop-up menu as translation or writing hints. We present a prototype writing assistant, TransAhead, that applies the method to a human-computer collaborative environment. Automatic and human evaluations show that novice translators or language learners substantially benefit from our system in terms of translation performance (i.e., translation accuracy and productivity) and language learning (i.e., collocation usage and grammar). In general, our methodology of inline grammar and text predictions or suggestions has great potential in the field of computer-assisted translation, writing, or even language learning.

Extracting Formulaic Expressions and Grammar and Edit Patterns to Assist Academic Writing

We present a method for extracting formulaic expressions, grammar patterns, and editing rules from a given corpus to assist learners in learning to write at the level required in English for Academic Purposes. In our method, sentences in a given corpus are parsed into chunks of base phrases, with the arguments sense disambiguated to derive syntactic and semantic grammar patterns. The method involves executing shallow parsing, transforming phrases into grammar patterns, and filtering and ranking grammar patterns for each headword. We applied the proposed method to a corpus annotated with writing errors and their corrections to derive editing rules. Experiments based on a large-scale academic English corpus and WikEd Error Corpus showed that the proposed method produces reasonable correct grammar patterns as well as edit rules. Thus, the method has the potential to assist learners in writing and self-editing.