From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis

Text preprocessing is often the first step in the pipeline of a Natural Language Processing (NLP) system, with potential impact in its final performance. Despite its importance, text preprocessing has not received much attention in the deep learning literature. In this paper we investigate the impact of simple text preprocessing decisions (particularly tokenizing, lemmatizing, lowercasing and multiword grouping) on the performance of a standard neural text classifier. We perform an extensive evaluation on standard benchmarks from text categorization and sentiment analysis. While our experiments show that a simple tokenization of input text is generally adequate, they also highlight significant degrees of variability across preprocessing techniques. This reveals the importance of paying attention to this usually-overlooked step in the pipeline, particularly when comparing different models. Finally, our evaluation provides insights into the best preprocessing practices for training word embeddings.Comment: Blackbox EMNLP 2018. 7 page

Derivative spectrophotometric determination of trace lead in alloys and biological samples after separation and preconcentration with the ion pair of 2-(5-bromo-2-pyridylazo)-5-diethylaminophenol and ammonium tetraphenylborate on microcrystalline

Lead is quantitatively retained on 2-(5-bromo-2-pyridylazo)-5-diethylaminophenol-ammonium tetraphenylborate with microcrystalline naphthalene or by a column method in the pH range 4.0–6.0 from a large volume of aqueous solutions of various samples. After filtration, the solid mass consisting of the lead complex and naphthalene was dissolved with 5 mL of dimethylformamide and the metal was determined by third derivative spectrophotometry. Lead complex can alternatively be quantitatively adsorbed on ammonium tetraphenylborate-naphthalene adsorbent packed in a column and determined similarly. About 0.2 mg of lead can be concentrated in a column from 300 mL of aqueous sample, where its concentration is as low as 0.7 ng/mL. The interference of a large number of anions and cations has been studied and the optimized conditions developed have been utilized for the trace determination of lead in various samples. KEY WORDS: Trace lead determination, Derivative spectrophotometry, 2-(5-Bromo-2-pyridylazo)-5-diethylamminophenol, Ammonium tetraphenylborate, Naphthalene  Bull. Chem. Soc. Ethiop. 2003, 17(2), 129-138.

Supervised Machine Learning for Signals Having RRC Shaped Pulses

Classification performances of the supervised machine learning techniques such as support vector machines, neural networks and logistic regression are compared for modulation recognition purposes. The simple and robust features are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals having root-raised-cosine shaped pulses are simulated in extreme noisy conditions having joint impurities of block fading, lack of symbol and sampling synchronization, carrier offset, and additive white Gaussian noise. The features are based on sample mean and sample variance of the imaginary part of the product of two consecutive complex signal values.Comment: 5 page

Evaluating the Evaluator: A Reflective Approach

Quality is a buzzword associated with education at all levels, especially in the leading private universities in Bangladesh. Quality education is made synonymous with the quality of classroom performance of the faculty members, which has led to the introduction of the quality development program through the evaluation of the faculty members by the students. This paper argues that Student Evaluation of Teaching (SET) program is bound to fail because it emphasizes only the faculty performance, missing the main point of student learning and thus bringing in strong reservation and silent resistance against it by the faculty members. The paper, therefore, advocates for Reflective approach of faculty development program that ensures quality assurance and enhancement without anxiety and resistance from the faculty members. Keywords: SET-Peer Observation of Teaching-POT Models-Reflective Approach of POT-Implementatio

Restoring Language to Literature Pedagogy: Towards an Interactive Approach

English literature teaching in Bangladeshi universities has remained to be a frustrating experience for the faculties. This “frustration” is often attributed to the poor understanding of “the intellectually unequipped” and ‘linguistically unprepared’ students. This study, to understand the faculty frustration, investigates the literature teaching approaches practiced in the classes. The study also examines the role of “linguistic skill” as the learning outcome in the lesson plan of the course teachers. Finally, the paper finds that literature teaching at the tertiary level in Bangladesh does not accommodate pedagogical approaches and the linguistic skill of the students is not given the required priority in the teaching process. This paper, therefore, recommends an Interactive Approach in which language is to be integrated as one of the learning outcomes.  

RELATIONSHIP BETWEEN JOB SATISFACTION AND PRODUCTIVITY AMONG LECTURERS OF SEYYED JAMALUDDIN TEACHER TRAINING INSTITUTE

Job satisfaction and productivity are the main focus of most organizations. Therefore, the present research was conducted to investigate the relationship between job satisfaction and productivity of lecturers in “Seyed Jamaluddin Afghan teacher training institute. It was a cross-sectional study. Descriptive and correlational methods were used to analyze the data. The population of the study was 200 lecturers of Seyed Jamaluddin Afghan teacher training institute out of which 131 were selected as a sample by a simple random method. To collect the data two standardized questionnaires, Smith, Kendall & Hulin’s (1969) job satisfaction questionnaire and the AGIO model 1980 manpower productivity questionnaire were used. The findings show that there was a meaningful relationship between the components of productivity and job satisfaction. There was no significant difference between male and female lecturers’ perceptions in terms of productivity. Furthermore, findings showed that lecturers with bachelor's degrees were more satisfied than lecturers with doctorate degrees

RELATIONSHIP BETWEEN JOB SATISFACTION AND PRODUCTIVITY AMONG LECTURERS OF SEYYED JAMALUDDIN TEACHER TRAINING INSTITUTE

Job satisfaction and productivity are the main focus of most organizations. Therefore, the present research was conducted to investigate the relationship between job satisfaction and productivity of lecturers in “Seyed Jamaluddin Afghan teacher training institute. It was a cross-sectional study. Descriptive and correlational methods were used to analyze the data. The population of the study was 200 lecturers of Seyed Jamaluddin Afghan teacher training institute out of which 131 were selected as a sample by a simple random method. To collect the data two standardized questionnaires, Smith, Kendall & Hulin’s (1969) job satisfaction questionnaire and the AGIO model 1980 manpower productivity questionnaire were used. The findings show that there was a meaningful relationship between the components of productivity and job satisfaction. There was no significant difference between male and female lecturers’ perceptions in terms of productivity. Furthermore, findings showed that lecturers with bachelor's degrees were more satisfied than lecturers with doctorate degrees

De-Conflated Semantic Representations

One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years. However, the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses. We propose a technique that tackles these problems by de-conflating the representations of words based on the deep knowledge that can be derived from a semantic network. Our approach provides multiple advantages in comparison to the previous approaches, including its high coverage and the ability to generate accurate representations even for infrequent word senses. We carry out evaluations on six datasets across two semantic similarity tasks and report state-of-the-art results on most of them