Anabela Barreiro

Generating Paraphrases of Human Intransitive Adjective Constructions with Port4NooJ

This paper details the integration into Port4NooJ of 15 lexicon-grammar tables describing the distributional properties of 4,248 human intransitive adjectives. The properties described in these tables enable the recognition and generation of adjectival constructions where the adjective has a predicative function. These properties also establish semantic relationships between adjective, noun and verb predicates, allowing new paraphrasing capabilities that were described in NooJ grammars. The new dictionary of human intransitive adjectives created by merging the information on those tables with the Port4NooJ homograph adjectives is comprised of 5,177 entries. The enhanced Port4NooJ is being used in eSPERTo, a NooJ-based paraphrase generation platform.

Integrating the Lexicon-Grammar of Predicate Nouns with Support Verb fazer into Port4NooJ

This paper describes the ongoing process of integrating approximately 3,000 predicate nouns into Port4NooJ, the Portuguese module for NooJ. The integration of these resources enables us to further extend the paraphrastic capabilities of eSPERTo paraphrasing system developed in the scope of a project with the same name. The integrated predicate nouns co-occur with the support verb fazer (do or make) and their syntactic and distributional properties are formalized in lexicon-grammar tables. These lexicon-grammar tables resulted in a standalone dictionary of predicate noun constructions and a few new grammars that can be used in paraphrase analysis and generation.

Machine Translation of Non-Contiguous Multiword Units

Non-adjacent linguistic phenomena such as non-contiguous multiwords and other phrasal units containing insertions, i.e., words that are not part of the unit, are difficult to process and remain a problem for NLP applications. Non-contiguous multiword units are common across languages and constitute some of the most important challenges to high quality machine translation. This paper presents an empirical analysis of non-contiguous multiwords, and highlights our use of the Logos Model and the Semtab function to deploy semantic knowledge to align non-contiguous multiword units with the goal to translate these units with high fidelity. The phrase level manual alignments illustrated in the paper were produced with the CLUE-Aligner, a CrossLanguage Unit Elicitation alignment tool.

eSPERTo’s Paraphrastic Knowledge Applied to Question-Answering and Summarization

This paper reports our first attempt of integrating eSPERTo’s paraphrastic engine, which is based on NooJ platform, with two application scenarios: a conversational agent, and a summarization system. We briefly describe eSPERTo’s base resources, and the necessary modifications to these resources that enabled the production of paraphrases required to feed both systems. Although the improvement observed in both scenarios is not significant, we present a detailed error analysis to further improve the achieved results in future experiments.