Maddalen Lopez de Lacalle

ArikIturri: an automatic question generator based on corpora and NLP techniques

Knowledge construction is expensive for Computer Assisted Assessment. When setting exercise questions, teachers use Test Makers to construct Question Banks. The addition of Automatic Generation to assessment applications decreases the time spent on constructing examination papers. In this article, we present ArikIturri, an Automatic Question Generator for Basque language test questions, which is independent from the test assessment application that uses it. The information source for this question generator consists of linguistically analysed real corpora, represented in XML mark-up language. ArikIturri makes use of NLP tools. The influence of the robustness of those tools and the used corpora is highlighted in the article. We have proved the viability of ArikIturri when constructing fill-in-the-blank, word formation, multiple choice, and error correction question types. In the evaluation of this automatic generator, we have obtained positive results as regards the generation process and its usefulness.

Comparing Different Approaches to Treat Translation Ambiguity in CLIR: Structured Queries vs. Target Co-occurrence Based Selection

Two main problems in Cross-language Information Retrieval are translation selection and the treatment of out-of-vocabulary terms. In this paper, we will be focusing on the problem concerning the translation selection. Structured queries and target co-occurrence-based methods seem to be the most appropriate approaches when parallel corpora are not available. However, there is no comparative study. In this paper we compare the results obtained using each of the aforementioned methods, we specify the weaknesses of each method, and finally we propose a hybrid method to combine both. In terms of mean average precision, results for Basque-English cross-lingual retrieval show that structured queries are the best approach both with long queries and short queries.

Elhuyar-IXA: semantic relatedness and cross-lingual passage retrieval

This article describes the participation of the joint Elhuyar-IXA group in the ResPubliQA exercise at QA&CLEF. In particular, we participated in the English-English monolingual task and in the Basque-English crosslingual one. Our focus has been threefold: (1) to check to what extent information retrieval (IR) can achieve good results in passage retrieval without question analysis and answer validation, (2) to check Machine Readable Dictionary (MRD) techniques for the Basque to English retrieval when faced with the lack of parallel corpora for Basque in this domain, and (3) to check the contribution of semantic relatedness based on WordNet to expand the passages to related words. Our results show that IR provides good results in the monolingual task, that our crosslingual system performs lower than the monolingual runs, and that semantic relatedness improves the results in both tasks (by 6 and 2 points, respectively).

Predicate Matrix: automatically extending the semantic interoperability between predicate resources

This paper presents a novel approach to improve the interoperability between four semantic resources that incorporate predicate information. Our proposal defines a set of automatic methods for mapping the semantic knowledge included in WordNet, VerbNet, PropBank and FrameNet. We use advanced graph-based word sense disambiguation algorithms and corpus alignment methods to automatically establish the appropriate mappings among their lexical entries and roles. We study different settings for each method using SemLink as a gold-standard for evaluation. The results show that the new approach provides productive and reliable mappings. In fact, the mappings obtained automatically outnumber the set of original mappings in SemLink. Finally, we also present a new version of the Predicate Matrix, a lexical-semantic resource resulting from the integration of the mappings obtained by our automatic methods and SemLink.

Estimating translation probabilities from the web for structured queries on CLIR

We present two methods for estimating replacement probabilities without using parallel corpora. The first method proposed exploits the possible translation probabilities latent in Machine Readable Dictionaries (MRD). The second method is more robust, and exploits context similarity-based techniques in order to estimate word translation probabilities using the Internet as a bilingual comparable corpus. The experiments show a statistically significant improvement over non weighted structured queries in terms of MAP by using the replacement probabilities obtained with the proposed methods. The context similarity-based method is the one that yields the most significant improvement.