Automatic processing of code-mixed social media content

Code-mixing or language-mixing is a linguistic phenomenon where multiple language mix together during conversation. Standard natural language processing (NLP) tools such as part-of-speech (POS) tagger and parsers perform poorly because such tools are generally trained with monolingual content. Thus there is a need for code-mixed NLP. This research focuses on creating a code-mixed corpus in English-Hindi-Bengali and using it to develop a world-level language identifier and a POS tagger for such code-mixed content. The first target of this research is word-level language identification. A data set of romanised and code-mixed content written in English, Hindi and Bengali was created and annotated. Word-level language identification (LID) was performed on this data using dictionaries and machine learn- ing techniques. We find that among a dictionary-based system, a character-n-gram based linear model, a character-n-gram based first order Conditional Random Fields (CRF) and a recurrent neural network in the form of a Long Short Term Memory (LSTM) that consider words as well as characters, LSTM outperformed the other methods. We also took part in the First Workshop of Computational Approaches to Code-Switching, EMNLP, 2014 where we achieved the highest token-level accuracy in the word-level language identification task of Nepali-English. The second target of this research is part-of-speech (POS) tagging. POS tagging methods for code- mixed data (e.g. pipeline and stacked systems and LSTM-based neural models) have been implemented, among them, neural approach outperformed the other approach. Further, we investigate building a joint model to perform language identification and POS tagging jointly. We compare between a factorial CRF (FCRF) based joint model and three LSTM-based multi-task models for word-level language identification and POS tagging. The neural models achieve good accuracy in language identification and POS tagging by outperforming the FCRF approach. Further- more, we found that it is better to go for a multi-task learning approach than to perform individual task (e.g. language identification and POS tagging) using neural approach. Comparison between the three neural approaches revealed that without using task-specific recurrent layers, it is possible to achieve good accuracy by careful handling of output layers for these two tasks e.g. LID and POS tagging

Lexical Normalization for Code-switched Data and its Effect on POS Tagging

Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of manynatural language processing tasks on social media. Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, despite its common use in social media. In this paper, we propose three normalization models specifically designed to handle code-switched data which we evaluate for two language pairs: Indonesian-English (Id-En) and Turkish-German (Tr-De). For the latter, we introduce novel normalization layers and their corresponding language ID and POS tags for the dataset, and evaluate the downstream effect of normalization on POS tagging. Results show that our CS-tailored normalization models outperform Id-En state of the art and Tr-De monolingual models, and lead to 5.4% relative performance increase for POS tagging as compared to unnormalized input

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network