Considerations about learning Word2Vec

AbstractDespite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real capabilities. In particular, to our knowledge, no author seems to have analysed in detail how learning may be affected by the various choices of hyperparameters. In this work, we try to shed some light on various issues focusing on a typical dataset. It is shown that the learning rate prevents the exact mapping of the co-occurrence matrix, that Word2Vec is unable to learn syntactic relationships, and that it does not suffer from the problem of overfitting. Furthermore, through the creation of an ad-hoc network, it is also shown how it is possible to improve Word2Vec directly on the analogies, obtaining very high accuracy without damaging the pre-existing embedding. This analogy-enhanced Word2Vec may be convenient in various NLP scenarios, but it is used here as an optimal starting point to evaluate the limits of Word2Vec

Optimized Realization of Bayesian Networks in Reduced Normal Form using Latent Variable Model

Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable, even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. An online version of the classic batch learning algorithm is also analyzed, showing very similar results (in an unsupervised context); which is essential even if multilevel structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood (ML) algorithms. The results are discussed with particular reference to a Latent Variable Model (LVM) structure.Comment: 20 pages, 8 figure

Combination of pharmacotherapy and lidocaine analgesic block of the peripheral trigeminal branches for trigeminal neuralgia: a pilot study

Classical trigeminal neuralgia (CTN) is treated predominantly by pharmacotherapy but side effects and unsuccessful occurs. The current study was carried out to evaluate the therapeutic effect of combination of pharmacotherapy and lidocaine block. Thirteen patients with CTN managed with pharmacotherapy were recruited and assigned either to no additional treatment (Group I) or to additional analgesic block (Group II). The primary endpoint was the reduction in the frequency of pain episodes in a month assessed at 30 and 90 days. Comparisons of measurements of pain, general health and depression scales were secondary endpoints. The results from the follow-up visits at 30 and 90 days showed the Group II to have larger reduction in the frequency of pain and exhibited a bigger improvement in the scores of the pain, general health and depression scales. The results from this preliminary study suggest a clinical benefit of the combination of pharmacotherapy and lidocaine block

Intent Classification in Question-Answering Using LSTM Architectures

Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI). Since the complete solution to the problem of finding a generic answer still seems far away, the wisest thing to do is to break down the problem by solving single simpler parts. Assuming a modular approach to the problem, we confine our research to intent classification for an answer, given a question. Through the use of an LSTM network, we show how this type of classification can be approached effectively and efficiently, and how it can be properly used within a basic prototype responder.Comment: Presented at the 2019 Italian Workshop on Neural Networks (WIRN'19) - June 201

Synthesis, self-assembly-behavior and biomolecular recognition properties of thyminyl dipeptides

This article describes the synthesis of Thy-(Phe-Phe) and Thy-(Tyr-Tyr), two thymine-bearing dipeptides based on L-phenylalanine and L-tyrosine, the circular dichroism (CD), UV and dynamic light scattering (DLS) characterization of their self-assemblies, and a CD study of their interaction with nucleic acids (using homoadenine DNA and RNA) and serum proteins (employing BSA as model protein). DLS studies, alongside with CD and UV investigations conducted on aqueous solutions of the derivatives under different concentration and temperature conditions, showed the formation of extensive molecular architectures with hydrodynamic mean diameters higher than 300 nm, with Thy-(Tyr-Tyr) forming at pH=7.5 particularly large and stable networks, involving multiple units, connected by H-bonding, aromatic and hydrophobic interactions. Finally, the findings of our study suggested that Thy-(Phe-Phe) and Thy-(Tyr-Tyr), very stable in human serum, were able to bind BSA protein altering its secondary structure

Re-discovery of the top quark at the LHC and first measurements

This paper describes the top quark physics measurements that can be performed with the first LHC data in the ATLAS and CMS experiments.Comment: 6 pages, 2 figures. Talk given at `V Workshop Italiano sulla Fisica pp a LHC', Perugia, Italy, 30 January - 2 February 200

Monte Carlo generators for top quark physics at the LHC

We review the main features of Monte Carlo generators for top quark phenomenology and present some results for t-tbar and single-top signals and backgrounds at the LHC.Comment: 7 pages, 5 figures. Talk given at `V Workshop Italiano sulla Fisica pp a LHC', Perugia, Italy, 30 January - 2 February 2008. References update

An Analysis of Word2Vec for the Italian Language

Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In this work, analysing the semantic capacity of the Word2Vec algorithm, an embedding for the Italian language is produced. Parameter setting such as the number of epochs, the size of the context window and the number of negatively backpropagated samples is explored.Comment: Presented at the 2019 Italian Workshop on Neural Networks (WIRN'19) - June 201