Role of Discourse Information in Urdu Sentiment Classification: A Rule-Based Method and Machine Learning Technique
In this article, we present a rule-based approach for transliterating two mostly used orthographies in Sorani Kurdish. Our work consists of detecting each character in a word by removing the possible ambiguities and mapping it into the target orthography. We describe different challenges in Kurdish text mining and propose novel ideas concerning the transliteration task for Sorani Kurdish. Our transliteration system, named Wergor, achieves 82.79% overall precision and more than 99% in detecting the double-usage characters. We also present a manually transliterated corpus for Kurdish.
Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems. With low-resource language pairs that do not have available and well-developed pronunciation lexicons, grapheme-to-phoneme models are particularly useful. These models are based on initial alignments between grapheme source and phoneme target sequences. Inspired by sequence-to-sequence recurrent neural network-based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pre-trained source and target embeddings to overcome the transliteration problem for a low-resource languages pair. Evaluation and experiments involving French and Vietnamese showed that with only a small bilingual pronunciation dictionary available for training the transliteration models, promising results were obtained with a large increase BLEU-scores and a reduction in translation error rate (TER) and phoneme error rate (PER). Moreover, we compared our proposed neural network-based transliteration approach with a statistical one.
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 5th most spoken language, and has become the 4th 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 more up-to-date literature survey. The aim of this paper 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 paper 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 paper 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.
A key element in computational discourse analysis is the design of a formal representation for the discourse structure of a text. With machine learning being the dominant method, it is important to identify a discourse representation that can be used to perform large-scale annotation. This survey provides a systematic analysis of existing discourse representation theories to evaluate whether they are suitable for annotation of Chinese text. Specifically, the two properties, expressiveness and practicality, are introduced to compare representations based on rhetorical relations and representations based on entity relations. The comparison systematically reveals linguistic and computational characteristics of the theories. After that, we conclude that none of existing theories are quite suitable for scalable Chinese discourse annotation because they are not both expressive and practical. Therefore, a new discourse representation needs to be proposed, which should balance the expressiveness and practicality, and cover rhetorical relations and entity relations. Inspired by the conclusions, this survey discusses some preliminary proposals on how to represent the discourse structure that are worth pursuing.
Temporality has significantly contributed to the various Natural Language Processing and Information Retrieval applications. In this paper, we first create a lexical knowledge-base in Hindi by identifying the temporal orientation of word senses based on their definition and then use this resource to detect underlying temporal orientation of the sentences. In order to create the resource, we propose a semi-supervised learn- ing framework, where each synset of the Hindi WordNet is classified into one of the five categories, namely past, present, future, neutral and atemporal. The algorithm initiates learning with a set of seed synsets and then iterates following different expansion strategies, viz. probabilistic expansion based on classifier?s confidence and semantic distance based measures. We manifest the usefulness of the resource that we build on an external task, viz. sentence-level temporal classification. The underlying idea is that a temporal knowledge- base can help in classifying the sentences according to their inherent temporal properties. Experiments on two different domains, viz. General and Twi er show very interesting results.
Modern Standard Arabic, as well as Arabic dialect languages, are usually written without diacritics. The absence of these marks constitute a real problem in the automatic processing of these data by NLP tools. Indeed, writing Arabic without diacritics introduces several types of ambiguity. Firstly, a word without diacratics could have many possible meanings depending on their diacritization. Secondly, undiacritized surface forms of an Arabic word might have as many as 200 readings depending on the complexity of its morphology . In fact, the agglutination property of Arabic might produce a problem that can only be resolved using diacritics. Thirdly, without diacritics a word could have many possible POS instead of one. This is the case with the words that have the same spelling and POS tag but a different lexical sense, or words that have the same spelling but different POS tags and lexical senses . Finally, there is ambiguity at the grammatical level (syntactic ambiguity). In this paper, we propose the first work that investigates the automatic diacritization of Tunisian Dialect texts. We first describe our annotation guidelines and procedure. Then, we propose two major models, namely a statistical machine translation (SMT) and a discriminative model as a sequence classification task based on CRFs (Conditional Random Fields). In the second approach, we integrate POS features to influence the generation of diacritics. Diacritics restoration was performed at both the word and the character levels. The results showed high scores of automatic diacritization based on the CRF system (WER 21.44% for CRF and WER 34.6% for SMT).
Dependency parsing is a fundamental problem in natural language processing. We introduce a novel dependancy parsing framework called head pointing based dependancy parsing. In this framework, we cast Korean dependency parsing problem to a statistical head pointing and arc labeling problem. To address the problem, a novel neural network called Multitask Pointer Networks is devised for a neural sequential head pointing and type labeling architecture. Our approach does not require any hand-crafting features or language-specific rules to parse dependency. Furthermore it shows state-of-the-art performance in Korean dependency parsing.
The number of possible word forms is theoretically infinite in agglutinative languages. This brings the out-of-vocabulary (OOV) issue for part-of-speech (PoS) tagging in agglutinative languages. Since the inflectional morphology does not change the PoS tag of a word, we propose to learn stems along with PoS tags simultaneously. Therefore, we aim to overcome the sparsity problem by reducing the word forms into their stems. We adopt a Bayesian model that is fully unsupervised. We build a Hidden Markov Model for PoS tagging where the stems are emitted through hidden states. Several versions of the model are introduced in order to observe the effects of the different dependencies throughout the corpus; such as the dependency between stems and PoS tags or the dependency between PoS tags and affixes. Additionally, we use neural word embeddings to estimate the semantic similarity between the word form and the stem. We use the semantic similarity as prior information to discover the actual stem of a word since the inflection does not change the meaning of a word. We compare our models with other unsupervised stemming and PoS tagging models on Turkish, Hungarian, Finnish, Basque, and English. The results show that a joint model for PoS tagging and stemming improves upon an independent PoS tagger and stemmer in agglutinative languages.