Data Sets: Word Embeddings Learned from Tweets and General Data

A word embedding is a low-dimensional, dense and real- valued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually gener- ated from a large text corpus. The embedding of a word cap- tures both its syntactic and semantic aspects. Tweets are short, noisy and have unique lexical and semantic features that are different from other types of text. Therefore, it is necessary to have word embeddings learned specifically from tweets. In this paper, we present ten word embedding data sets. In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data. The general data consist of news articles, Wikipedia data and other web data. These ten embedding models were learned from about 400 million tweets and 7 billion words from the general text. In this paper, we also present two experiments demonstrating how to use the data sets in some NLP tasks, such as tweet sentiment analysis and tweet topic classification tasks

Synthetic Text Generation using Hypergraph Representations

Generating synthetic variants of a document is often posed as text-to-text transformation. We propose an alternate LLM based method that first decomposes a document into semantic frames and then generates text using this interim sparse format. The frames are modeled using a hypergraph, which allows perturbing the frame contents in a principled manner. Specifically, new hyperedges are mined through topological analysis and complex polyadic relationships including hierarchy and temporal dynamics are accommodated. We show that our solution generates documents that are diverse, coherent and vary in style, sentiment, format, composition and facts

Unsupervised Domain Adaptation using Lexical Transformations and Label Injection for Twitter Data

Domain adaptation is an important and widely studied problem in natural language processing. A large body of literature tries to solve this problem by adapting models trained on the source domain to the target domain. In this paper, we instead solve this problem from a dataset perspective. We modify the source domain dataset with simple lexical transformations to reduce the domain shift between the source dataset distribution and the target dataset distribution. We find that models trained on the transformed source domain dataset performs significantly better than zero-shot models. Using our proposed transformations to convert standard English to tweets, we reach an unsupervised part-of-speech (POS) tagging accuracy of 92.14% (from 81.54% zero shot accuracy), which is only slightly below the supervised performance of 94.45%. We also use our proposed transformations to synthetically generate tweets and augment the Twitter dataset to achieve state-of-the-art performance for POS tagging.Comment: Accepted at WASSA at ACL 202

Synthetic Document Generator for Annotation-free Layout Recognition

Analyzing the layout of a document to identify headers, sections, tables, figures etc. is critical to understanding its content. Deep learning based approaches for detecting the layout structure of document images have been promising. However, these methods require a large number of annotated examples during training, which are both expensive and time consuming to obtain. We describe here a synthetic document generator that automatically produces realistic documents with labels for spatial positions, extents and categories of the layout elements. The proposed generative process treats every physical component of a document as a random variable and models their intrinsic dependencies using a Bayesian Network graph. Our hierarchical formulation using stochastic templates allow parameter sharing between documents for retaining broad themes and yet the distributional characteristics produces visually unique samples, thereby capturing complex and diverse layouts. We empirically illustrate that a deep layout detection model trained purely on the synthetic documents can match the performance of a model that uses real documents

Bayesian Hierarchical Models for Counterfactual Estimation

Counterfactual explanations utilize feature perturbations to analyze the outcome of an original decision and recommend an actionable recourse. We argue that it is beneficial to provide several alternative explanations rather than a single point solution and propose a probabilistic paradigm to estimate a diverse set of counterfactuals. Specifically, we treat the perturbations as random variables endowed with prior distribution functions. This allows sampling multiple counterfactuals from the posterior density, with the added benefit of incorporating inductive biases, preserving domain specific constraints and quantifying uncertainty in estimates. More importantly, we leverage Bayesian hierarchical modeling to share information across different subgroups of a population, which can both improve robustness and measure fairness. A gradient based sampler with superior convergence characteristics efficiently computes the posterior samples. Experiments across several datasets demonstrate that the counterfactuals estimated using our approach are valid, sparse, diverse and feasible

InProC: Industry and Product/Service Code Classification

Determining industry and product/service codes for a company is an important real-world task and is typically very expensive as it involves manual curation of data about the companies. Building an AI agent that can predict these codes automatically can significantly help reduce costs, and eliminate human biases and errors. However, unavailability of labeled datasets as well as the need for high precision results within the financial domain makes this a challenging problem. In this work, we propose a hierarchical multi-class industry code classifier with a targeted multi-label product/service code classifier leveraging advances in unsupervised representation learning techniques. We demonstrate how a high quality industry and product/service code classification system can be built using extremely limited labeled dataset. We evaluate our approach on a dataset of more than 20,000 companies and achieved a classification accuracy of more than 92\%. Additionally, we also compared our approach with a dataset of 350 manually labeled product/service codes provided by Subject Matter Experts (SMEs) and obtained an accuracy of more than 96\% resulting in real-life adoption within the financial domain

Is Chaalia/Pan Masala harmful for health? practices and knowledge of children of schools in Mahmoodabad and Chanesar Goth, Karachi

OBJECTIVE: To determine the practices and knowledge of harmful effects regarding use of Chaalia and Pan Masala in three schools of Mahmoodabad and Chanesar Goth, Jamshed Town, Karachi, Pakistan. METHODS: To achieve the objective a cross-sectional design was used in three government schools of Mahmoodabad and Chanesar Goth, Jamshed Town, Karachi. Students of either gender drawn from these schools fulfilling the inclusion and exclusion criteria were interviewed using a pre-coded structured questionnaire. Along with demographic data, questions regarding frequency of Chaalia and Pan Masala use, practices of this habit in friends and family and place of procurement of these substances, were inquired. Knowledge was assessed about harmful effects and its source of information. In addition, practices in relation to that knowledge were assessed. RESULTS: A total of 370 students were interviewed over a period of six weeks, of which 205 (55.4%) were boys. The ages of the students were between 10 and 15 years. Thirty one percent of the fathers and 62% of the mothers were uneducated. The frequency of use of any brand of Chaalia was found to be 94% and that of Pan Masala was 73.8%. Eighty five percent of them were regular users. A large majority (88%) procured the substances themselves from near their homes. Ninety five percent of the children had friends with the same habits. Eighty four percent were using the substances in full knowledge of their families. Chaalia was considered harmful for health by 96% and Pan Masala by 60%. Good taste was cited as a reason for continuing the habit by 88.5% of the children and use by friends by 57%. Knowledge about established harmful effects was variable. Knowledge about harmful effects was high in both daily and less than daily users . CONCLUSION: The frequency of habits of Chaalia and Pan Masala chewing, by school children in lower socio-economic areas is extremely high. The probable reasons for this high frequency are taste, the widespread use of these substances by family members and friends, low cost and easy availability