Joint Approaches for Learning Word Representations from Text Corpora and Knowledge Bases

The work presented in this thesis is directed at investigating the possibility of combining text corpora and Knowledge Bases (KBs) for learning word representations. More specifically, the aim was to propose joint approaches that leverage the two types of resources for the purpose of enhancing the word meaning representations. The main research question to be answered was “Is it possible to enhance the word representations by jointly incorporating text corpora and KBs into the word representations learning process? If so, what are the aspects of word meaning that can be enhanced by combining those two types of resources? ”. The primary contribution of the thesis is three main joint approaches for learning word representations: (i) Joint Representation Learning for Additional Evidence (JointReps), (ii) Joint Hierarchical Word Representation (HWR) and (iii) Sense-Aware Word Representations (SAWR). The JointReps was founded to improve the overall semantic representation of words. To this end, it sought additional evidence from a KB to the co-occurrence statistics in the corpus. In particular, JointReps enforced two words that are in a particular semantic relationship in the KB to have similar word representations. The HWR approach was then proposed to learn word representations in a specific order to encode the hierarchical information in a KB in the learnt representations. The HWR considered not only the hypernym relations that exist between words in a KB, but also contextual information in a text corpus. Specifically, given a training corpus and a KB, HWR learnt word representations that simultaneously encoded the hierarchical structure in the KB as well as the co-occurrence statistics between pairs of words in the corpus. A particularly novel aspect of the HWR approach was that it exploits the full hierarchical path of words existing in the KB. The SAWR approach was then introduced to consider not only word representations but also the different senses (different meanings) associated with each word. The SAWR required the learnt representations to predict the word and the senses accurately. It learnt the sense-aware word representations jointly using both unlabelled and sense-labelled text corpora. The approaches were comprehensively analysed and evaluated in various standard and newly-proposed tasks using a wide range of benchmark datasets. The evaluation was conducted to compare the quality of the learnt word representations by the proposed approaches with word representations learnt by sole-resource baselines and previously proposed joint approaches in the literature. All the proposed joint approaches have proven to be effective for enhancing the learnt word representations. More specifically, the proposed joint approaches were found to report significant improvements over the approaches that use only one type of resources and the previously proposed joint approaches

Joint Word Representation Learning Using a Corpus and a Semantic Lexicon.

Methods for learning word representations using large text corpora have received much attention lately due to their impressive performancein numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection.Despite their success, these data-driven word representation learning methods do not considerthe rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNetthat represent the meanings of words by defining the various relationships that exist among the words in a language.We consider the question, can we improve the word representations learnt using a corpora by integrating theknowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predictsthe co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon.We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus.Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into wordrepresentations on several benchmark datasets for semantic similarity and word analogy

Preparation, spectroscopic and thermal studies on the zinc(II), cadmium(II), tin(II), lead(II) and antimony(III) creatinine complexes

ABSTRACT. Zinc(II), cadmium(II), tin(II), lead(II) and antimony(III) complexes of creatinine with the composition of [M(creat)2Xn].xH2O, (X = Cl or NO3; n = 2-6) were prepared. The complexes were characterized by analytical and spectral methods. The analysis of FT-IR and Raman spectra helps to understand the coordination properties of the creatinine ligand and to determine the probable structure of the complexes. The shift in the resonances of cyclic NH proton in the 1H NMR when compared to the ligand indicated that cyclic nitrogen coordinates. Conductivity measurements in DMSO suggested that the complexes are non-electrolytes. Thermal decomposition behavior of the complexes was also discussed.   KEY WORDS: Creatinine, TGA/DTA, Metal complexation, Raman spectroscopy   Bull. Chem. Soc. Ethiop. 2022, 36(4), 831-842.                                                              DOI: https://dx.doi.org/10.4314/bcse.v36i4.

Synthesis and spectroscopic characterizations of manganese(II), iron(III), copper(II) and zinc(II) hydrazine complexes as catalytic activity agents

ABSTRACT. This article deals with the preparation and coordination of NH2—NH2 hydrazine molecule compounds. The hydrazine sulfate complexes of Mn(II), Fe(III), Cu(II), and Zn(II) were prepared. These complexes were characterized by elemental, infrared, conduction, electron absorption spectroscopy, magnetic susceptibility, thermogravimetric analyses, X-ray powder diffraction (XRD) patterns and atomic force microscopy (AFM) studies. The magnetic measurements were confirmed that the Mn(II), Fe(III), Cu(II), and Zn(II) hydrazine complexes have an octahedral geometric structure. Thermogravimetric and its differential thermogravimetric analysis referred that all complexes passed through two-to-three thermal degradation steps with solid metal sulfate as a residual product. The infrared spectra inferred that the NH2—NH2 ligand forms complexes through nitrogen atoms of the—NH2 moiety, while the elemental analysis indicates [M(NH2—NH2)3]SO4 (where M = Mn(II), Cu(II), and Zn(II)) while the iron(III) complexes have the [Fe2(NH2—NH2)4(SO4)2]SO4 formula of coordination compounds, NH2—NH2 acts as a double bond. Both XRD and AFM analysis deduced that the synthesized hydrazine metal complexes were found to be in nano scale range 10—30 nm.                 KEY WORDS: Hydrazine, FTIR, AFM, XRD, Transition metals Bull. Chem. Soc. Ethiop. 2022, 36(1), 33-44.                                                                    DOI:   https://dx.doi.org/10.4314/bcse.v36i1.4                                                    &nbsp

Four new tin(II), uranyl(II), vanadyl(II), and zirconyl(II) alloxan biomolecule complexes: synthesis, spectroscopic and thermal characterizations

ABSTRACT. The alloxan as a biomolecule ligand has been utilized to synthesize thermodynamically and kinetically stabilized four new tin(II), uranyl(II), vanadyl(II), and zirconyl(II) complexes. In the complexes, tin(II) ion present is in tetrahedral arrangement, zirconyl and vanadyl(II) ions present are in square pyramid feature but uranyl(II) ion present is in octahedral arrangement and all are coordinated by two bidentate alloxan ligand in complexes. The synthesized alloxan ligand coordinate with central metal(II) ion through oxygen in position C2=O and the nitrogen in position N1 developing a 4-membered chelate ring. Synthesized Sn(II), UO2(II), VO(II), and ZrO(II) complexes via bidentate ligand have been accurately described by various spectroscopic techniques like elemental analysis (C, H, N, metal), conductivity measurements, FT-IR, UV-Vis, 1H-NMR, and TGA. The kinetic thermodynamic parameters such as: E*, ΔH*, ΔS* and ΔG* were calculated using Coats and Redfern and Horowitz and Metzger equations.   KEY WORDS: Alloxan, Metal ions, Spectroscopy, Ligand, Coordination, Thermogravimetry   Bull. Chem. Soc. Ethiop. 2022, 36(2), 373-385.    DOI: https://dx.doi.org/10.4314/bcse.v36i2.11                                                          &nbsp

Jointly learning word embeddings using a corpus and a knowledge base

Methods for representing the meaning of words in vector spaces purely using the information distributed in text corpora have proved to be very valuable in various text mining and natural language processing (NLP) tasks. However, these methods still disregard the valuable semantic relational structure between words in co-occurring contexts. These beneficial semantic relational structures are contained in manually-created knowledge bases (KBs) such as ontologies and semantic lexicons, where the meanings of words are represented by defining the various relationships that exist among those words. We combine the knowledge in both a corpus and a KB to learn better word embeddings. Specifically, we propose a joint word representation learning method that uses the knowledge in the KBs, and simultaneously predicts the co-occurrences of two words in a corpus context. In particular, we use the corpus to define our objective function subject to the relational constrains derived from the KB. We further utilise the corpus co-occurrence statistics to propose two novel approaches, Nearest Neighbour Expansion (NNE) and Hedged Nearest Neighbour Expansion (HNE), that dynamically expand the KB and therefore derive more constraints that guide the optimisation process. Our experimental results over a wide-range of benchmark tasks demonstrate that the proposed method statistically significantly improves the accuracy of the word embeddings learnt. It outperforms a corpus-only baseline and reports an improvement of a number of previously proposed methods that incorporate corpora and KBs in both semantic similarity prediction and word analogy detection tasks

Synthesis, spectroscopic characterizations and DFT studies on the metal complexes of azathioprine immunosuppressive drug

ABSTRACT. A complex of the immunosuppressive drug azathioprine with Cr(II), Mn(II), Fe(II), Zn(II), Cu(II), Ni(II), and Co(II) were synthesized and characterized through spectroscopic and thermal studies. The infrared spectra show the coordination of azathioprine via N(9) to the metal, also, the range around 640–650 cm−1 remains unchanged in the complexes, indicating the possibility that the ether group may not be involved in the binding. Thermogravimetric analysis (TG), thermogravimetric derivational analysis (DTG), and differential thermogravimetric analysis (DTA) have been studied in the temperature range from 0 °C to 1000 °C. The study of pyrolysis showed that all complexes decompose in more than one step and that the final decomposition product is metal oxide. The DFT (density functional theory) with B3LYP/6-31G++ level of theory was used to study the optimized geometry, HOMO→LUMO energy gap, and molecular electrostatic potential map of azathioprine before and after deprotonation.                 KEY WORDS: Azathioprine, Spectral study, Thermal study, Decomposition products, DFT Bull. Chem. Soc. Ethiop. 2022, 36(1), 73-84.                                                                   DOI: https://dx.doi.org/10.4314/bcse.v36i1.