Habibi - a multi Dialect multi National Arabic Song Lyrics Corpus

This paper introduces Habibi the first Arabic Song Lyrics corpus. The corpus comprises more than 30,000 Arabic song lyrics in 6 Arabic dialects for singers from 18 different Arabic countries. The lyrics are segmented into more than 500,000 sentences (song verses) with more than 3.5 million words. I provide the corpus in both comma separated value (csv) and annotated plain text (txt) file formats. In addition, I converted the csv version into JavaScript Object Notation (json) and eXtensible Markup Language (xml) file formats. To experiment with the corpus I run extensive binary and multi-class experiments for dialect and country-of-origin identification. The identification tasks include the use of several classical machine learning and deep learning models utilising different word embeddings. For the binary dialect identification task the best performing classifier achieved a testing accuracy of 93%. This was achieved using a word-based Convolutional Neural Network (CNN) utilising a Continuous Bag of Words (CBOW) word embeddings model. The results overall show all classical and deep learning models to outperform our baseline, which demonstrates the suitability of the corpus for both dialect and country-of-origin identification tasks. I am making the corpus and the trained CBOW word embeddings freely available for research purposes

OSMAN:a novel Arabic readability metric

We present OSMAN (Open Source Metric for Measuring Arabic Narratives) - a novel open source Arabic readability metric and tool. It allows researchers to calculate readability for Arabic text with and without diacritics. OSMAN is a modified version of the conventional readability formulas such as Flesch and Fog. In our work we introduce a novel approach towards counting short, long and stress syllables in Arabic which is essential for judging readability of Arabic narratives. We also introduce an additional factor called “Faseeh” which considers aspects of script usually dropped in informal Arabic writing. To evaluate our methods we used Spearman’s correlation metric to compare text readability for 73,000 parallel sentences from English and Arabic UN documents. The Arabic sentences were written with the absence of diacritics and in order to count the number of syllables we added the diacritics in using an open source tool called Mishkal. The results show that OSMAN readability formula correlates well with the English ones making it a useful tool for researchers and educators working with Arabic text

Arabic multi-document text summarisation

Multi-document summarisation is the process of producing a single summary of a collection of related documents. Much of the current work on multi-document text summarisation is concerned with the English language; relevant resources are numerous and readily available. These resources include human generated (gold-standard) and automatic summaries. Arabic multi-document summarisation is still in its infancy. One of the obstacles to progress is the limited availability of Arabic resources to support this research. When we started our research there were no publicly available Arabic multi-document gold-standard summaries, which are needed to automatically evaluate system generated summaries. The Document Understanding Conference (DUC) and Text Analysis Conference (TAC) at that time provided resources such as gold-standard extractive and abstractive summaries (both human and system generated) that were only available in English. Our aim was to push forward the state-of-the-art in Arabic multi-document summarisation. This required advancements in at least two areas. The first area was the creation of Arabic test collections. The second area was concerned with the actual summarisation process to find methods that improve the quality of Arabic summaries. To address both points we created single and multi-document Arabic test collections both automatically and manually using a commonly used English dataset and by having human participants. We developed extractive language dependent and language independent single and multi-document summarisers, both for Arabic and English. In our work we provided state-of-the-art approaches for Arabic multi-document summarisation. We succeeded in including Arabic in one of the leading summarisation conferences the Text Analysis Conference (TAC). Researchers on Arabic multi-document summarisation now have resources and tools that can be used to advance the research in this field

Effectiveness of query expansion in searching the Holy Quran

Modern Arabic text is written without diacritical marks (short vowels), which causes considerable ambiguity at the word level in the absence of context. Exceptional from this is the Holy Quran, which is endorsed with short vowels and other marks to preserve the pronunciation and hence, the correctness of sensing its words. Searching for a word in vowelized text requires typing and matching all its diacritical marks, which is cumbersome and preventing learners from searching and understanding the text. The other way around, is to ignore these marks and fall in the problem of ambiguity. In this paper, we provide a novel diacritic-less searching approach to retrieve from the Quran relevant verses that match a user’s query through automatic query expansion techniques. The proposed approach utilizes a relational database search engine that is scalable, portable across RDBMS platforms, and provides fast and sophisticated retrieval. The results are presented and the applied approach reveals future directions for search engines

Does equity analyst research lack rigor and objectivity? Evidence from conference call questions and research notes

Research questions the rigor and objectivity of analysts’ research due to the institutional structures in which they operate (Fogarty and Rogers, 2005 Accounting, Organisations and Society). However, insights from psychology highlight the need to condition this conclusion on the incentives for attributional search. Based on social cognition theory, we test whether the degree of diligence and criticality evident in analyst research is higher (lower) for negative(nonnegative) schema-discrepant events. We evaluate this prediction against the null hypothesis that analyst research consistently lacks rigor and objectivity. We use earnings surprises as our schema-discrepant conditioning event, and examine the content of analysts’ conference call questions and research notes to assess the properties of their research. We find that levels of rigor and objectivity are statistically and economically higher for research conducted in response to negative earnings surprises. Findings are consistent with analysts’ innate cognitive processing response counteracting institutional considerations when attributional search incentives are strong. Results also reveal non-trivial levels of rigor and objectivity in response to non-negative schema-discrepant earnings news. Differences in the properties of analysts’ work are also evident for spoken and written modalities

Creating and validating multilingual semantic representations for six languages:expert versus non-expert crowds

Creating high-quality wide-coverage multilingual semantic lexicons to support knowledge-based approaches is a challenging time-consuming manual task. This has traditionally been performed by linguistic experts: a slow and expensive process. We present an experiment in which we adapt and evaluate crowdsourcing methods employing native speakers to generate a list of coarse-grained senses under a common multilingual semantic taxonomy for sets of words in six languages. 451 non-experts (including 427 Mechanical Turk workers) and 15 expert participants semantically annotated 250 words manually for Arabic, Chinese, English, Italian, Portuguese and Urdu lexicons. In order to avoid erroneous (spam) crowdsourced results, we used a novel taskspecific two-phase filtering process where users were asked to identify synonyms in the target language, and remove erroneous senses

Detecting document structure in a very large corpus of UK financial reports

In this paper we present the evaluation of our automatic methods for detecting and extracting document structure in annual financial reports. The work presented is part of the Corporate Financial Information Environment (CFIE) project in which we are using Natural Language Processing (NLP) techniques to study the causes and consequences of corporate disclosure and financial reporting outcomes. We aim to uncover the determinants of financial reporting quality and the factors that influence the quality of information disclosed to investors beyond the financial statements. The CFIE consists of the supply of information by firms to investors, and the mediating influences of information intermediaries on the timing, relevance and reliability of information available to investors. It is important to compare and contrast specific elements or sections of each annual financial report across our entire corpus rather than working at the full document level. We show that the values of some metrics e.g. readability will vary across sections, thus improving on previous research based on full texts

Creating and validating multilingual semantic representations for six languages:expert versus non-expert crowds

Creating high-quality wide-coverage multilingual semantic lexicons to support knowledge-based approaches is a challenging time-consuming manual task. This has traditionally been performed by linguistic experts: a slow and expensive process. We present an experiment in which we adapt and evaluate crowdsourcing methods employing native speakers to generate a list of coarse-grained senses under a common multilingual semantic taxonomy for sets of words in six languages. 451 non-experts (including 427 Mechanical Turk workers) and 15 expert participants semantically annotated 250 words manually for Arabic, Chinese, English, Italian, Portuguese and Urdu lexicons. In order to avoid erroneous (spam) crowdsourced results, we used a novel taskspecific two-phase filtering process where users were asked to identify synonyms in the target language, and remove erroneous senses

Exploring Abstractive Text Summarisation for Podcasts:A Comparative Study of BART and T5 Models

Podcasts have become increasingly popular in recent years, resulting in a massive amount of audio content being produced every day. Efficient summarisation of podcast episodes can enable better content management and discovery for users. In this paper, we explore the use of abstractive text summarisation methods to generate high-quality summaries of podcast episodes. We use pre-trained models, BART and T5, to fine-tune on a dataset of Spotify's 100K podcast. We evaluate our models using automated metrics and human evaluation, and find that the BART model fine-tuned on the podcast dataset achieved a higher ROUGE-1 and ROUGE-L score compared to other models, while the T5 model performed better in terms of semantic meaning. The human evaluation indicates that both models produced high-quality summaries that were well received by participants. Our study demonstrates the effectiveness of abstractive summarisation methods for podcast episodes and offers insights for improving the summarisation of audio content