Eduard Barbu

Strudel: A Corpus‐Based Semantic Model Based on Properties and Types

Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meaning in terms of undifferentiated bags of words or topical dimensions. This has led some to question their psychological plausibility (Murphy, 2002;Schunn, 1999). We present here a fully automatic method for extracting a structured and comprehensive set of concept descriptions directly from an English part-of-speech-tagged corpus. Concepts are characterized by weighted properties, enriched with concept-property types that approximate classical relations such as hypernymy and function. Our model outperforms comparable algorithms in cognitive tasks pertaining not only to concept-internal structures (discovering properties of concepts, grouping properties by property type) but also to inter-concept relations (clustering into superordinates), suggesting the empirical validity of the property-based approach.

An Unsupervised Method for Automatic Translation Memory Cleaning.

We address the problem of automatically cleaning a large-scale Translation Memory (TM) in a fully unsupervised fashion, i.e. without human-labelled data. We approach the task by: i) designing a set of features that capture the similarity between two text segments in different languages, ii) use them to induce reliable training labels for a subset of the translation units (TUs) contained in the TM, and iii) use the automatically labelled data to train an ensemble of binary classifiers. We apply our method to clean a test set composed of 1,000 TUs randomly extracted from the English-Italian version of MyMemory, the world’s largest public TM. Our results show competitive performance not only against a strong baseline that exploits machine translation, but also against a state-of-the-art method that relies on human-labelled data.

Ensembles of Classifiers for Cleaning Web Parallel Corpora and Translation Memories.

The last years witnessed an increasing interest in the automatic methods for spotting false translation units in translation memories. This problem presents a great interest to industry as there are many translation memories that contain errors. A closely related line of research deals with identifying sentences that do not align in the parallel corpora mined from the web. The task of spotting false translations is modeled as a binary classification problem. It is known that in certain conditions the ensembles of classifiers improve over the performance of the individual members. In this paper we benchmark the most popular ensemble of classifiers: Majority Voting, Bagging, Stacking and Ada Boost at the task of spotting false translation units for translation memories and parallel web corpora. We want to know if for this specific problem any ensemble technique improves the performance of the individual classifiers and if there is a difference between the data in translation memories and parallel web corpora with respect to this task.