From MultiJEDI to MOUSSE: Two ERC Projects for innovating multilingual disambiguation and semantic parsing of text

In this paper we present two interrelated projects funded by the European Research Council (ERC) aimed at addressing and over- coming the current limits of lexical semantics: MultiJEDI (Section 2) and MOUSSE (Section 4). We also present the results of Babelscape (Section 3), a Sapienza spin-off company with the goal of making the project outcomes sustainable in the long ter

Domain Adaptation for Text Classification with Weird Embeddings

Pre-trained word embeddings are often used to initialize deep learning models for text classification, as a way to inject precomputed lexical knowledge and boost the learning process. However, such embeddings are usually trained on generic corpora, while text classification tasks are often domain-specific. We propose a fully automated method to adapt pre-trained word embeddings to any given classification task, that needs no additional resource other than the original training set. The method is based on the concept of word weirdness, extended to score the words in the training set according to how characteristic they are with respect to the labels of a text classification dataset. The polarized weirdness scores are then used to update the word embeddings to reflect task-specific semantic shifts. Our experiments show that this method is beneficial to the performance of several text classification tasks in different languages

WordNet as an Ontology for Generation

International audienceIn this paper we propose WordNet as an alternative to ontologies for the purpose of natural language generation. In particular , the synset-based structure of WordNet proves useful for the lexicalization of concepts , by providing ready lists of lemmas for each concept to generate

Towards Generating Text from Discourse Representation Structures

International audienceWe argue that Discourse Representation Structures form a suitable level of language-neutral meaning representation for micro planning and surface realisation. DRSs can be viewed as the output of macro planning, and form the rough plan and structure for generating a text. We present the first ideas of building a large DRS corpus that enables the development of broad-coverage, robust text generators. A DRS-based generator imposes various challenges on micro-planning and surface realisation, including generating referring expressions , lexicalisation and aggregation