Syntactic discontinuous reduplication with antonymic pairs: a case study from Italian

This article aims at giving a comprehensive account of a so far undescribed reduplicative pattern in Italian named syntactic discontinuous reduplication with antonymic pairs (SDRA). This pattern, characterized by the non-contiguous repetition of the same element within a larger fixed configuration defined by two spatial antonyms, can be schematized as <Xi Adv1 Xi Adv2>, where Adv1 and Adv2 are antonyms (e.g., di qua ‘here’ ∼ di là ‘there’). After describing its formal and functional properties, based on naturally occurring data extracted from the Italian Web 2016 corpus, the SDRA is analyzed as an independent ‘construction’ in the Construction Grammar sense. This construction is claimed to convey a general value of ‘plurality’ and to have developed a polysemy network of daughter constructions expressing more specific functions such as ‘distributivity,’ ‘related variety,’ and ‘dispersion.’ In addition, we propose considering the SDRA a ‘multiple source construction,’ originating from the blending of two independent constructions: syntactic reduplication and irreversible binomials with antonymic adverbs. Finally, we discuss SDRA-like patterns in other typologically different languages (Russian, Modern Hebrew, Mandarin Chinese, German), pointing out similarities and differences, and paving the way to a more systematic study of discontinuous reduplication in a crosslinguistic perspective.

Novelty Detection: A Perspective from Natural Language Processing

The quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural Language Processing (NLP), Novelty Detection refers to finding text that has some new information to offer with respect to whatever is earlier seen or known. With the exponential growth of information all across the web, there is an accompanying menace of redundancy. A considerable portion of the web contents are duplicates, and we need efficient mechanisms to retain new information and filter out redundant ones. However, detecting redundancy at the semantic level and identifying novel text is not straightforward because the text may have less lexical overlap yet convey the same information. On top of that, non-novel/redundant information in a document may have assimilated from multiple source documents, not just one. The problem surmounts when the subject of the discourse is documents, and numerous prior documents need to be processed to ascertain the novelty/non-novelty of the current one in concern. In this work, we build upon our earlier investigations for document-level novelty detection and present a comprehensive account of our efforts towards the problem. We explore the role of pre-trained Textual Entailment (TE) models to deal with multiple source contexts and present the outcome of our current investigations. We argue that a multi-premise entailment task is one close approximation towards identifying semantic-level non-novelty. Our recent approach either performs comparably or achieves significant improvement over the latest reported results on several datasets and across several related tasks (paraphrasing, plagiarism, rewrite). We critically analyze our performance with respect to the existing state-of-the-art and show the superiority and promise of our approach for future investigations. We also present our enhanced dataset TAP-DLND 2.0 and several baselines to the community for further researchon document-level novelty detection.

Automated methodology for optimal selection of the minimum electrode subset for accurate EEG source localization based on Genetic Algorithm optimization

High-density Electroencephalography (HD-EEG) has been proven to be the most accurate option to estimate the neural activity inside the brain. Although multiple studies report the effect of electrode number on source localization for specific sources and specific electrode configurations, the electrodes for each configuration have been manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, where electrodes were not selected according to their contribution to accuracy. In this work, an optimization-based study aimed to determine the minimum number of electrodes and identify optimal combinations of electrodes that can keep the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single and multiple source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that minimize (1) the localization error for each source and (2) the number of required EEG electrodes. It can be used for evaluating the source localization quality of low-density EEG systems (consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG dataset with known ground-truth. The experimental results shown that selected electrode combinations with 6 electrodes can obtain for a single source case, an equal or better accuracy than HD-EEG (with more than 200 channels) when reconstructing a particular brain activity in more than 88% of the cases (in synthetic signals) and 63% (in real signals), and more than 88% and 73% of the cases when considering combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that an equal or better accuracy than HD-EEG with 231 electrodes was attained in at least 58%, 76%, and 82% of the cases, when using optimized combinations of 8, 12, and 16 electrodes, respectively. Additionally, in such electrode numbers a lower mean error and standard deviation than with 231 electrodes was obtained.