A New Perspective on Reusing Semantic Resources

Well trained linguists manage to capture semantic behavior of words in various annotated corpora. Using them as training data, semantic relations can be discovered by intelligent systems using supervised machine learning techniques. What if we have short deadlines and limited human and financial possibilities that prevent us from building such a valuable training corpus for our own language? If such a corpus already exists for any other language, we could make use of this treasure and reproduce it for the language we need. This paper proposes an import method, which transfers semantic annotation (which could be semantic roles, named entity, sentiments, etc.) from an annotated resource to another language, using comparable texts. The case of semantic role annotation transfer from English to Romanian is discussed

Mining Social Media to Extract Structured Knowledge through Semantic Roles

Semantics is a well-kept secret in texts, accessible only to humans. Artificial Intelligence struggles to enrich machines with human-like features, therefore accessing this treasure and sharing it with computers is one of the main challenges that the computational linguistics domain faces nowadays. In order to teach computers to understand humans, language models need to be specified and created from human knowledge. While still far from completely decoding hidden messages in political speeches, computer scientists and linguists have joined efforts in making the language easier to be understood by machines. This paper aims to introduce the VoxPopuli platform, an instrument to collect user generated content, to analyze it and to generate a map of semantically-related concepts by capturing crowd intelligence

Moving-Time and Moving-Ego Metaphors from a Translational and Contrastivelinguistic Perspective

This article is concerned with some cross-linguistic asymmetries in the use of two types of time metaphors, the Moving-Time and the Moving-Ego metaphor. The latter metaphor appears to be far less well-entrenched in languages such as Croatian or Hungarian, i.e. some of its lexicalizations are less natural than their alternatives based on the Moving- Time metaphor, while some others are, unlike their English models, downright unacceptable. It is argued that some of the differences can be related to the status of the fictive motion construction and some restrictions on the choice of verbs in that construction

Target-Side Context for Discriminative Models in Statistical Machine Translation

Discriminative translation models utilizing source context have been shown to help statistical machine translation performance. We propose a novel extension of this work using target context information. Surprisingly, we show that this model can be efficiently integrated directly in the decoding process. Our approach scales to large training data sizes and results in consistent improvements in translation quality on four language pairs. We also provide an analysis comparing the strengths of the baseline source-context model with our extended source-context and target-context model and we show that our extension allows us to better capture morphological coherence. Our work is freely available as part of Moses.Comment: Accepted as a long paper for ACL 201

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010