Automatic Part-of-speech Research Articles

The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

Abstract Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people’s opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which is required to label words according to grammatical categories. The challenge of analyzing the Arabic language has found considerable research interest, but now the challenge is amplified with the addition of social media dialects. While numerous morphological analyzers and PoS taggers were proposed for Modern Standard Arabic (MSA), we are now witnessing an increased interest in applying those techniques to the Arabic dialect that is prominent in social media. Indeed, social media texts (e.g. posts, comments, and replies) differ significantly from MSA texts in terms of vocabulary and grammatical structure. Such differences call for reviewing the PoS tagging methods to adapt social media texts. Furthermore, the lack of sufficiently large and diverse social media text corpora constitutes one of the reasons that automatic PoS tagging of social media content has been rarely studied. In this paper, we address those limitations by proposing a novel Arabic social media text corpus that is enriched with complete PoS information, including tags, lemmas, and synonyms. The proposed corpus constitutes the largest manually annotated Arabic corpus to date, with more than 5 million tokens, 238,600 MSA texts, and words from Arabic social media dialect, collected from 65,000 online users’ accounts. Furthermore, our proposed corpus was used to train a custom Long Short-Term Memory deep learning model and showed excellent performance in terms of sentiment classification accuracy and F1-score. The obtained results demonstrate that the use of a diverse corpus that is enriched with PoS information significantly enhances the performance of social media analysis techniques and opens the door for advanced features such as opinion mining and emotion intelligence.

Implementation of Kadazan Tagger Based on Brill's Method

We present and evaluate the implementation of Part of Speech ( POS) Tagging for the Kadazan The main purpose of this study is to develop an aut omatic POS tagging for the Kadazan language, which had never, been developed before. POS tagging can tag the Kadazan corpus automatically and can help r problem of th is language. The achieve a better and higher accuracy or at least si milar to that of the other tagging approaches such as the statistical This approach can transform the tags based on the p res number of objectives were set in order to achieve t he main purpose of this study. Firstly, to apply the lexical and contextual rules for this language. Secondly, to implement the Brill's algorithm based on the set of rules and finally to determine the eff ectiveness of the Kadazan Part of Speech by using t his approach. The tagging system had been trained using four Kadazan corpuses containing 5663 words in all. Based on the evaluation results, the tagging system had achieved around 93% accuracy. and evaluate the implementation of Part of Speech ( POS) Tagging for the Kadazan language by using the Transformation-based approach. The main purpose of this study is to develop an aut omatic POS tagging for the Kadazan language, which had never, been developed before. POS tagging can tag the Kadazan corpus automatically and can help r educe the disambiguation is language. The implementation of this approach in this study is to achieve a better and higher accuracy or at least si milar to that of the other tagging approaches such as the statistical and the original rule-based approach. This approach can transform the tags based on the p res cribed set of rules. A number of objectives were set in order to achieve t he main purpose of this study. Firstly, to apply the lexical and contextual rules for this language. Secondly, to implement the Brill's algorithm based on the set of rules and finally to determine ectiveness of the Kadazan Part of Speech by using t his approach. The tagging system had been trained using four Kadazan corpuses containing 5663 words in all. Based on the evaluation results, the tagging system had achieved ; kadazan language ; Part of Speech tagger; rule-based based . Part of Speech (POS) tagging is a system that read the text in some languages and assign POS such as noun, verb, adjective, adver b, pronoun, etc . to every word in the text (corpus). The tagging process coul d be linked with morphological process such as the formation of adjective from verb. For charm', which is tagged as a verb, could be transformed int o is suffixed to that word which would then become . In Natural Language Processing, POS tagging is important in order to show how the words could be related to each othe r and how the ordered structures of the sentence could help resolve the a mbiguity problem in different kinds of analysis levels. POS tagging had been use d in many applications such machine translation, speech recognition, informatio n retrieval, dictionary (Wordnet) and so on. Hence, the importance of POS tagging cannot be ignored

Minimizing Human Intervention for Constructing Korean Part-of-Speech Tagged Corpus

The construction of annotated corpora requires considerable manual effort. This paper presents a pragmatic method to minimize human intervention for the construction of Korean part-of-speech (POS) tagged corpus. Instead of focusing on improving the performance of conventional automatic POS taggers, we devise a discriminative POS tagger which can selectively produce either a single analysis or multiple analyses based on the tagging reliability. The proposed approach uses two decision rules to judge the tagging reliability. Experimental results show that the proposed approach can effectively control the quality of corpus and the amount of manual annotation by the threshold value of the rule.

Word Based Chinese Semantic Role Labeling with Semantic Chunking

Recently, with the development of Chinese semantically annotated corpora, e.g. the Chinese Proposition Bank, the Chinese role labeling (SRL) has been boosted. However, the Chinese SRL researchers now focus on the transplant of existing statistical machine learning methods which have been proven to be effective on English. In this paper, we have established a word based Chinese SRL system, which is quite different from the previous based ones. We use semantic to represent our new method. Semantic chunking is named because of its similarity with syntactic chunking. The difference is that chunking is used to identify and classify the chunks, i.e. the roles, only with the word-level information. Based on chunking, the process of SRL is changed from parsing — role identification — role classification, to semantic chunk identification — chunk With the elimination of the stage, the SRL task can get rid of the dependency on parsing, which is the bottleneck both of speed and precision. The experiments have shown that the chunking based method outperforms previously best-reported results on Chinese SRL, if the word segmentation and part-of-speech (POS) tagging are both correct. On the automatic word segmentation and POS tagging, our method decreases a little. The greatest advantage of chunking method is that it saves a large amount of time. Besides these, we also carry out some experiments only for role classification. These experiments have shown that only with the word-level features, the performance of role classification can still be very high, which proves that the syntactic structural information is not indispensable.

Parse tree-based machine translation for less-used languages

The article describes a method that enhances translation performance of language pairs with a less used source language and a widely used target language. We propose a method that enables the use of parse tree based statistical translation algorithms for language pairs with a less used source language and a widely used target language. Automatic part of speech (POS) tagging algorithms have become accurate to the extent of efficient use in many tasks. Most of these methods are quite easily implementable in most world languages. The method is divided in two parts; the first part constructs alignments between POS tags of source sentences and induced parse trees of target language. The second part searches through trained data and selects the best candidates for target sentences, the translations. The method was not fully implemented due to time constraints; the training part was implemented and incorporated into a functional translation system; the inclusion of a word alignment model into the translation part was not implemented. The empirical evaluation addressing the quality of trained data was carried out on a full implementation of the presented training algorithms and the results confirm the employability of the method.

The effect of learner errors on POS tag errors during automatic POS tagging

The use of a corpus as a language resource is enhanced when it is part of speech (POS) tagged. There are a number of computer tagging programs (taggers) available. In order to decide which taggers to use to POS tag the Tswana Learner English Corpus (TLEC), the performance of three taggers (TOSCA-ICLE, Brill tagger, and CLAWS) was evaluated on a small sample of the corpus. An evaluation of an unedited version of the sample indicated that learner spelling errors contributed substantially to tagging errors. All spelling errors were then corrected and the edited sample was retagged. With the spelling errors removed, the performance of all three taggers improved. This article reports on the influence of the other learner errors on the performance of the taggers. It was found that 38% of the tag errors made by CLAWS on the edited sample were due to learner errors. In the case of TOSCA 19%, and in the case of Brill 14%, of the tag errors were due to learner errors.