Mixed Script Identification Using Automated DNN Hyperparameter Optimization

Mixed script identification is a hindrance for automated natural language processing systems. Mixing cursive scripts of different languages is a challenge because NLP methods like POS tagging and word sense disambiguation suffer from noisy text. This study tackles the challenge of mixed script identification for mixed-code dataset consisting of Roman Urdu, Hindi, Saraiki, Bengali, and English. The language identification model is trained using word vectorization and RNN variants. Moreover, through experimental investigation, different architectures are optimized for the task associated with Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Experimentation achieved the highest accuracy of 90.17 for Bi-GRU, applying learned word class features along with embedding with GloVe. Moreover, this study addresses the issues related to multilingual environments, such as Roman words merged with English characters, generative spellings, and phonetic typing.

Normalization of Transliterated Mongolian Words Using Seq2Seq Model with Limited Data

The huge increase in social media use in recent years has resulted in new forms of social interaction, changing our daily lives. Due to increasing contact between people from different cultures as a result of globalization, there has also been an increase in the use of the Latin alphabet, and as a result a large amount of transliterated text is being used on social media. In this study, we propose a variety of character level sequence-to-sequence (seq2seq) models for normalizing noisy, transliterated text written in Latin script into Mongolian Cyrillic script, for scenarios in which there is a limited amount of training data available. We applied performance enhancement methods, which included various beam search strategies, N-gram-based context adoption, edit distance-based correction and dictionary-based checking, in novel ways to two basic seq2seq models. We experimentally evaluated these two basic models as well as fourteen enhanced seq2seq models, and compared their noisy text normalization performance with that of a transliteration model and a conventional statistical machine translation (SMT) model. The proposed seq2seq models improved the robustness of the basic seq2seq models for normalizing out-of-vocabulary (OOV) words, and most of our models achieved higher normalization performance than the conventional method. When using test data during our text normalization experiment, our proposed method which included checking each hypothesis during the inference period achieved the lowest word error rate (WER = 13.41%), which was 4.51% fewer errors than when using the conventional SMT method.

Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text

We proposed an application and data variations-independent, generic social media Textual Variations Handler (TVH) to deal with a wide range of noise in textual data generated in various social media (SM) applications for enhanced text analysis. The aim is to build an effective hybrid normalization technique that ensures the use of useful information of the noisy text in its intended form instead of filtering them out to analyze SM text better. The proposed TVH performs context-aware text normalization based on intended meaning to avoid the wrong word substitution. We integrate the TVH with state-of-the-art (SOTA) deep-learning-based text analysis methods to enhance their performance for noisy SM text data. The proposed scheme shows promising improvement in the text analysis of informal SM text in terms of precision, recall, accuracy, and F1-score in simulation.

From General Language Understanding to Noisy Text Comprehension

Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The proposed research establishes effective methods for improving the comprehension of noisy texts. For this, we propose a new generic methodology to derive a diverse set of sentence vectors combining and extracting various linguistic characteristics from latent representations of multi-layer, pre-trained language models. Further, we clearly establish how BERT, a state-of-the-art pre-trained language model, comprehends the linguistic attributes of Tweets to identify appropriate sentence representations. Five new probing tasks are developed for Tweets, which can serve as benchmark probing tasks to study noisy text comprehension. Experiments are carried out for classification accuracy by deriving the sentence vectors from GloVe-based pre-trained models and Sentence-BERT, and by using different hidden layers from the BERT model. We show that the initial and middle layers of BERT have better capability for capturing the key linguistic characteristics of noisy texts than its latter layers. With complex predictive models, we further show that the sentence vector length has lesser importance to capture linguistic information, and the proposed sentence vectors for noisy texts perform better than the existing state-of-the-art sentence vectors.

Bootstrapping Dependency Treebank of Urdu Noisy Text

This paper describes how bootstrapping was used to extend the development of the Urdu Noisy Text dependency treebank. To overcome the bottleneck of manually annotating corpus for a new domain of user-generated text, MaltParser, an opensource, data-driven dependency parser, is used to bootstrap the treebank in semi-automatic manner for corpus annotation after being trained on 500 tweet Urdu Noisy Text Dependency Treebank. Total four bootstrapping iterations were performed. At the end of each iteration, 300 Urdu tweets were automatically tagged, and the performance of parser model was evaluated against the development set. 75 automatically tagged tweets were randomly selected out of pre-tagged 300 tweets for manual correction, which were then added in the training set for parser retraining. Finally, at the end of last iteration, parser performance was evaluated against test set. The final supervised bootstrapping model obtains a LA of 72.1%, UAS of 75.7% and LAS of 64.9%, which is a significant improvement over baseline score of 69.8% LA, 74% UAS, and 62.9% LAS

Universal Dependencies for Urdu Noisy Text

In this paper, the process of creating a Dependency Treebank for tweetsin Urdu,a morphologically rich and less-resourced languageis described. The 500 Urdu tweets treebank iscreated by manually annotating the treebank withlemma, POS tags, morphological and syntacticrelations using the Universal Dependencies annotation scheme, adopted to the peculiarities of Urdu social media text. annotation process is evaluated through Inter-annotator agreement for dependency relations and total agreement of 94.5% and resultant weighted Kappa = 0.876was observed. The treebank is evaluated through 10-fold cross validation using Maltparserwith various feature settings. Results show average UAS score of 74%, LAS score of 62.9% and LA score of 69.8%.

An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance Detection

A large fraction of textual data available today contains various types of “noise,” such as OCR noise in digitized documents, noise due to informal writing style of users on microblogging sites, and so on. To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i.e., for cleaning different kinds of noise in the text. There have been several efforts towards cleaning or normalizing noisy text; however, many of the existing text normalization methods are supervised and require language-dependent resources or large amounts of training data that is difficult to obtain. We propose an unsupervised algorithm for text normalization that does not need any training data/human intervention. The proposed algorithm is applicable to text over different languages and can handle both machine-generated and human-generated noise. Experiments over several standard datasets show that text normalization through the proposed algorithm enables better retrieval and stance detection, as compared to that using several baseline text normalization methods.

Developing a POS Tagged Corpus of Urdu Tweets

Processing of social media text like tweets is challenging for traditional Natural Language Processing (NLP) tools developed for well-edited text due to the noisy nature of such text. However, demand for tools and resources to correctly process such noisy text has increased in recent years due to the usefulness of such text in various applications. Literature reports various efforts made to develop tools and resources to process such noisy text for various languages, notably, part-of-speech (POS) tagging, an NLP task having a direct effect on the performance of other successive text processing activities. Still, no such attempt has been made to develop a POS tagger for Urdu social media content. Thus, the focus of this paper is on POS tagging of Urdu tweets. We introduce a new tagset for POS-tagging of Urdu tweets along with the POS-tagged Urdu tweets corpus. We also investigated bootstrapping as a potential solution for overcoming the shortage of manually annotated data and present a supervised POS tagger with an accuracy of 93.8% precision, 92.9% recall and 93.3% F-measure.