Several applications, such as text-to-SQL and computational fact checking, exploit the relationship between relational data and natural language text. However, state of the art solutions simply fail in managing "data-ambiguity", i.e., the case when there are multiple interpretations of the relationship between text and data. Given the ambiguity in language, text can be mapped to different subsets of data, but existing training corpora only have examples in which every sentence/question is annotated precisely w.r.t. the relation. This unrealistic assumption leaves the target applications unable to handle ambiguous cases. To tackle this problem, we present an end-to-end solution that, given a table D, generates examples that consist of text, annotated with its data evidence, with factual ambiguities w.r.t. D. We formulate the problem of profiling relational tables to identify row and attribute data ambiguity. For the latter, we propose a deep learning method that identifies every pair of data ambiguous attributes and a label that describes both columns. Such metadata is then used to generate examples with data ambiguities for any input table. To enable scalability, we finally introduce a SQL approach that can generate millions of examples in seconds. We show the high accuracy of our solution in profiling relational tables and report on how our automatically generated examples lead to drastic quality improvements in two fact-checking applications, including a website with thousands of users, and in a text-to-SQL system.

Abstract In the last few years, the natural language processing community has witnessed advances in neural representations of free texts with transformer-based language models (LMs). Given the importance of knowledge available in tabular data, recent research efforts extend LMs by developing neural representations for structured data. In this article, we present a survey that analyzes these efforts. We first abstract the different systems according to a traditional machine learning pipeline in terms of training data, input representation, model training, and supported downstream tasks. For each aspect, we characterize and compare the proposed solutions. Finally, we discuss future work directions.

Applications such as computational fact checking and data-to-text generation exploit the relationship between relational data and natural language text. Despite promising results in these areas, state of the art solutions simply fail in managing "data-ambiguity", i.e., the case when there are multiple interpretations of the relationship between the textual sentence and the relational data. To tackle this problem, we introduce Pythia, a system that, given a relational table D, generates textual sentences that contain factual ambiguities w.r.t. the data in D. Such sentences can then be used to train target applications in handling data-ambiguity. In this demonstration, we first show how our system generates data ambiguous sentences for a given table in an unsupervised fashion by data profiling and query generation. We then demonstrate how two existing applications benefit from Pythia's generated sentences, improving the state-of-the-art results. The audience will interact with Pythia by changing input parameters in an interactive fashion, including the upload of their own dataset to see what data ambiguous sentences are generated for it.

In the last few years, the natural language processing community witnessed advances in neural representations of free texts with transformer-based language models (LMs). Given the importance of knowledge available in relational tables, recent research efforts extend LMs by developing neural representations for tabular data. In this tutorial, we present these proposals with two main goals. First, we introduce to a database audience the potentials and the limitations of current models. Second, we demonstrate the large variety of data applications that benefit from the transformer architecture. The tutorial aims at encouraging database researchers to engage and contribute to this new direction, and at empowering practitioners with a new set of tools for applications involving text and tabular data.

Success of Natural Language Processing (NLP) models, just like all advanced machine learning models, rely heavily on large -scale lexical resources. For English, English WordNet (EWN) is a leading example of a large-scale resource that has enabled advances in Natural Language Understanding (NLU) tasks such as word sense disambiguation, question answering, sentiment analysis, and emotion recognition. EWN includes sets of cognitive synonyms called synsets, which are interlinked by means of conceptual-semantic and lexical relations and where each synset expresses a distinct concept. However, other languages are still lagging behind in having large-scale and rich lexical resources similar to EWN. In this article, we focus on enabling the development of such resources for Arabic. While there have been efforts in developing an Arabic WordNet (AWN), the current version of AWN has its limitations in size and in lacking transliteration standards, which are important for compatibility with Arabic NLP tools. Previous efforts for extending AWN resulted in a lexicon, called ArSenL, that overcame the size and the transliteration standard limitation but was limited in accuracy due to the heuristic approach that only considered surface matching between the English definitions from the Standard Arabic Morphological Analyzer (SAMA) and EWN synset terms, and that resulted in inaccurate mapping of Arabic lemmas to EWN’s synsets. Furthermore, there has been limited exploration of other expansion methods due to expensive manual validation needed. To address these limitations of simultaneously having large-scale size with high accuracy and standard representations, the mapping problem is formulated as a link prediction problem between a large-scale Arabic lexicon and EWN, where a word in one lexicon is linked to a word in another lexicon if the two words are semantically related. We use a semi-supervised approach to create a training dataset by finding common terms in the large-scale Arabic resource and AWN. This set of data becomes implicitly linked to EWN and can be used for training and evaluating prediction models. We propose the use of a two-step Boosting method, where the first step aims at linking English translations of SAMA’s terms to EWN’s synsets. The second step uses surface similarity between SAMA’s glosses and EWN’s synsets. The method results in a new large-scale Arabic lexicon that we call ArSenL 2.0 as a sequel to the previously developed sentiment lexicon ArSenL. A comprehensive study covering both intrinsic and extrinsic evaluations shows the superiority of the method compared to several baseline and state-of-the-art link prediction methods. Compared to previously developed ArSenL, ArSenL 2.0 included a larger set of sentimentally charged adjectives and verbs. It also showed higher linking accuracy on the ground truth data compared to previous ArSenL. For extrinsic evaluation, ArSenL 2.0 was used for sentiment analysis and showed, here, too, higher accuracy compared to previous ArSenL.

Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion resources for Arabic. To close this gap, we examine the class of models used for English and that do not require extensive use of NLP or opinion resources. In particular, we consider the Recursive Auto Encoder (RAE). However, RAE models are not as successful in Arabic as they are in English, due to their limitations in handling the morphological complexity of Arabic, providing a more complete and comprehensive input features for the auto encoder, and performing semantic composition following the natural way constituents are combined to express the overall meaning. In this article, we propose A R ecursive Deep Learning Model for O pinion M ining in A rabic (AROMA) that addresses these limitations. AROMA was evaluated on three Arabic corpora representing different genres and writing styles. Results show that AROMA achieved significant performance improvements compared to the baseline RAE. It also outperformed several well-known approaches in the literature.

The proliferation of powerful smart devices is revolutionizing mobile computing systems. A particular set of applications that is gaining wide interest is recommender systems. Recommender systems provide their users with recommendations on variety of personal and relevant items or activities. They can play a significant role in today's life whether in E-commerce or for daily decisions that we need to make. We introduce a hybrid approach for solving the problem of finding the ratings of unrated items in a user-item ranking matrix through a weighted combination of user-based and item-based collaborative filtering. The proposed technique provides improvements in addressing two major challenges of recommender systems: accuracy of recommender systems and sparsity of data by simultaneously incorporating users' correlations and items ones. The evaluation of the system shows superiority of the solution compared to stand-alone user-based collaborative filtering or item-based collaborative filtering.