Due to the detrimental consequences caused by cyberbullying, a great deal of research has been undertaken to propose effective techniques to resolve this reoccurring problem. The research presented ...in this paper is motivated by the fact that negative emotions can be caused by cyberbullying. This paper proposes cyberbullying detection models that are trained based on contextual, emotions and sentiment features. An Emotion Detection Model (EDM) was constructed using Twitter datasets that have been improved in terms of its annotations. Emotions and sentiment were extracted from cyberbullying datasets using EDM and lexicons based. Two cyberbullying datasets from Wikipedia and Twitter respectively were further improved by comprehensive annotation of emotion and sentiment features. The results show that anger, fear and guilt were the major emotions associated with cyberbullying. Subsequently, the extracted emotions were used as features in addition to contextual and sentiment features to train models for cyberbullying detection. The results demonstrate that using emotion features and sentiment has improved the performance of detecting cyberbullying by 0.5 to 0.6 recall. The proposed models also outperformed the state-of-the-art models by a 0.7 f1-score. The main contribution of this work is two-fold, which includes a comprehensive emotion-annotated dataset for cyberbullying detection, and an empirical proof of emotions as effective features for cyberbullying detection.
eXtensible Markup Language (XML) is used widely to transfer data among a wide variety of systems. Due to an increase in query workloads and management of larger datasets, centralized processing is no ...longer feasible for XML query processing. To address this issue, we propose a technique that improves XML query processing through query workload distribution. An effective distributed XML query processing can be affected by several criteria such as indexing, fragmentation, distribution strategy, and well as the query handling in the distributed servers. However, we believe that an efficient labeling mechanism and an inexpensive centralized query processors or a pruning method at dedicated servers contribute greatly to the overall performance of a distributed query processor. In this paper, we present an effective centralized pruning technique that is adopted into our proposed distributed XML query processing technique to process XML queries robustly. Experimental evaluations showed that the proposed distributed query processor superseded the performance of centralized query processor.
•We propose SemIE, Semantic-based Information Extraction and Mapping.•Our approach identifies significant relations and maps them to a semantic structure.•Our approach bootstraps training examples ...from a pair of structured documents.•The results show our approach outperforms current state-of-the-art system.•The results prove the effectiveness of our approach in handling complex sentences.
Open Information Extraction (OIE) systems focus on identifying and extracting general relations from text. Most OIE systems utilize simple linguistic structure, such as part-of-speech or dependency features, to extract relations and arguments from a sentence. These approaches are simple and fast to implement, but suffer from two main drawbacks: i) they are less effective to handle complex sentences with multiple relations and shared arguments, and ii) they tend to extract overly-specific relations.
This paper proposes an approach to Information Extraction called SemIE, which addresses both drawbacks. SemIE identifies significant relations from domain-specific text by utilizing a semantic structure that describes the domain of discourse. SemIE exploits the predicate-argument structure of a text, which is able to handle complex sentences. The semantics of the arguments are explicitly specified by mapping them to relevant concepts in the semantic structure.
SemIE uses a semi-supervised learning approach to bootstrap training examples that cover all relations expressed in the semantic structure. SemIE inputs pairs of structured documents and uses a Greedy Mapping module to bootstrap a full set of training examples. The training examples are then used to learn the extraction and mapping rules.
We evaluated the performance of SemIE by comparing it with OLLIE, a state-of-the-art OIE system. We tested SemIE and OLLIE on the task of extracting relations from text in the “movie” domain and found that on average, SemIE outperforms OLLIE. Furthermore, we also examined how the performance varies with sentence complexity and sentence length. The results prove the effectiveness of SemIE in handling complex sentences.
Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge ...is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed. Trustworthiness of facts in knowledge graph can be enhanced by the addition of metadata like the source of information, location and time of the fact occurrence. Since RDF does not support metadata for providing provenance and contextualization, an alternate method, RDF reification is employed by most of the knowledge graphs. RDF reification increases the magnitude of data as several statements are required to represent a single fact. Another limitation for applications that uses provenance data like in the medical domain and in cyber security is that not all facts in these knowledge graphs are annotated with provenance data. In this paper, we have provided an overview of prominent reification approaches together with the analysis of popular, general knowledge graphs Wikidata and YAGO4 with regard to the representation of provenance and context data. Wikidata employs qualifiers to include metadata to facts, while YAGO4 collects metadata from Wikidata qualifiers. However, facts in Wikidata and YAGO4 can be fetched without using reification to cater for applications that do not require metadata. To the best of our knowledge, this is the first paper that investigates the method and the extent of metadata covered by two prominent KGs, Wikidata and YAGO4.
With the emerging of social media as a platform of interaction, users are now connected with all around the world. This connection enhances dissemination of information from users. Examples of social ...platforms which are highly used among users are Facebook and Twitter. Recently WHO stated that dengue is currently rising among the countries in the World. In Malaysia, the number of dengue cases are currently rising at an alarming stage where it exceeds the number of last year’s dengue cases. Although traditional methods of distributing surveys and conducting awareness about dengue has been done, positive responses from those efforts are little. In this paper, we have surveyed the feasibility of engaging social media like Twitter for monitoring and preventing dengue. Tweets related to dengue will be analyzed using emotion detection techniques, aiming to identify tweets that generate positive emotion, which in return helpful for dengue monitoring and prevention. Since most of tweets are written in Malay language by Malaysians, a proper dictionary is needed to analyze the emotion of users. The outcome from this study will be beneficial for dengue prevention and monitoring by detecting how Malaysians are actually tweeting and reacting to dengue cases in Malaysia.
•Introduce Structural Semantic Annotation to improve English to Malay EBMT System.•Emphasize meaning structure preservation in the automated translation process.•Translation examples representation ...is extended with Structural Semantic Annotation.•Resolved the fragmentation and inconsistency issues in EBMT system.•The result of the English to Malay automated translation is improved.
The main tasks in Example-based Machine Translation (EBMT) comprise of source text decomposition, following with translation examples matching and selection, and finally adaptation and recombination of the target translation. As the natural language is ambiguous in nature, the preservation of source text’s meaning throughout these processes is complex and challenging. A structural semantics is introduced, as an attempt towards meaning-based approach to improve the EBMT system. The structural semantics is used to support deeper semantic similarity measurement and impose structural constraints in translation examples selection. A semantic compositional structure is derived from the structural semantics of the selected translation examples. This semantic compositional structure serves as a representation structure to preserve the consistency and integrity of the input sentence’s meaning structure throughout the recombination process. In this paper, an English to Malay EBMT system is presented to demonstrate the practical application of this structural semantics. Evaluation of the translation test results shows that the new translation framework based on the structural semantics has outperformed the previous EBMT framework.
Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge ...is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed. Therefore, the provenance of knowledge can assist in building up the trust of these knowledge graphs. In this paper, we have provided an analysis of popular, general knowledge graphs Wikidata and YAGO4 with regard to the representation of provenance and context data. Since RDF does not support metadata for providing provenance and contextualization, an alternate method, RDF reification is employed by most of the knowledge graphs. Trustworthiness of facts in knowledge graph can be enhanced by the addition of metadata like the source of information, location and time of the fact occurrence. Wikidata employs qualifiers to include metadata to facts, while YAGO4 collects metadata from Wikidata qualifiers. RDF reification increases the magnitude of data as several statements are required to represent a single fact. However, facts in Wikidata and YAGO4 can be fetched without using reification. Another limitation for applications that uses provenance data is that not all facts in these knowledge graphs are annotated with provenance data. Structured data in the knowledge graph is noisy. Therefore, the reliability of data in knowledge graphs can be increased by provenance data. To the best of our knowledge, this is the first paper that investigates the method and the extent of the addition of metadata of two prominent KGs, Wikidata and YAGO4.
Users interact using short-formed words and abbreviations and this results in a message full of noisy words that are not recognized by the system's knowledge. The aim of this research is to overcome ...the limitations that still bar the progression of normalizing Malay noisy words from social media platforms. The testing data gathered is 25,000; 15,000 Tweets from Twitter and 10,000 comments from Facebook respectively. Pre-processing steps were carried out to clean the entire dataset which consists of unique 179,786 words. 36,587 out-of-vocabulary (OOV) Malay terms were then extracted and checked against an in- vocabulary (IV) Malay corpus using the Levenshtein edit distance formula and character manipulation rules. The resultant output is 3,964 unique IV Malay words. Based on the results, the usage of edit distance and rules can be further improved to elevate the normalisation of the ever changing colloquial terms of the Malay language.
The impulsive headway in the use of XML attracted researchers to conduct researches on the optimization techniques of XML data management. One of the aspects that have been a challenge since then is ...the effective processing of user queries. Although many techniques have been proposed in the past, these techniques still suffer from large overhead due to buffering irrelevant results before producing the final output. Thus, in this paper, we propose a path query processing technique, DGReLab+, which evaluates queries without buffering irrelevant results. The evaluations revealed that DGReLab+ outperformed two other techniques which are TwigStack and QTwig.
Named entity recognition (NER) is a subtask in information extraction which aims to locate atomic element into predefined types. Various NER techniques and tools have been developed to fit the ...interest of the applications developed. However, most NER works carried out focus on non-fiction domain. Fiction based domain displays a complex context in locating its NE, specifically whereby its characters could be represented in diverse spectrums, ranging from living things (animals, plants, and person) to non-living things (vehicle, furniture). Motivated by a hypothesis such that there always exists verb specifically describes human being conduct, in this paper, we propose a NER system which aims to identify NEs that perform human activity based on verb analysis (VAHA) in an autonomous manner. More specifically, our approach attempts to identify dominant character (DC) by studying the nature of verb that associates with human activity via TreeTagger, Stanford packages and WordNet. Our experimental results validate our initial hypothesis that NEs can be accurately identified by referring to the associated verbs that associate with human activity. Our empirical study also proves that the approach is applicable to small text size articles. Another significant contribution of our approach is that it does not require training data set and anaphora resolution.
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