Identifying Urdu Complex Predication via Bigram Extraction

ABSTRACT A problem that crops up repeatedly in shallow and deep syntactic parsing approaches to South Asian languages like Urdu/Hindi is the proper treatment of complex predications. Problems for the NLP of complex predications are posed by their productiveness and the ill understood nature of the range of their combinatorial possibilities. This paper presents an investigation into whether fine-grained information about the distributional properties of nouns in N+V CPs can be identified by the comparatively simple process of extracting bigrams from a large "raw" corpus of Urdu. In gathering the relevant properties, we were aided by visual analytics in that we coupled our computational data analysis with interactive visual components in the analysis of the large data sets. The visualization component proved to be an essential part of our data analysis, particular for the easy visual identification of outliers and false positives. Another essential component turned out to be our language-particular knowledge and access to existing language-particular resources. Overall, we were indeed able to identify high frequency N-V complex predications as well as pick out combinations we had not been aware of before. However, a manual inspection of our results also pointed to a problem of data sparsity, despite the use of a large corpus

Hindi Complex Predicates: Linguistic and Computational Approaches

Complex predicates that comprise of a noun and verb e.g. yaad kar 'memory do; remember' are a productive class of multi-words in Hindi. In this thesis, we examine the challenges of identification and representation for these complex predicates in Hindi. We design and implement their representation in a lexical semantic resource as well as in lexicalized computational grammars. As productive multi-word predicates, their accurate identification is a necessity for natural language processing applications. We use a combination of linguistic and computational approaches to address these challenges. We use these methods to demonstrate the semi-automatic creation of subcategorization frames for Hindi and the development of classes for nominal predicates. Finally, we demonstrate how linguistic features and computational tools can be used in tandem to automatically identify complex predicates from unseen text

Handbook of Lexical Functional Grammar

Lexical Functional Grammar (LFG) is a nontransformational theory of linguistic structure, first developed in the 1970s by Joan Bresnan and Ronald M. Kaplan, which assumes that language is best described and modeled by parallel structures representing different facets of linguistic organization and information, related by means of functional correspondences. This volume has five parts. Part I, Overview and Introduction, provides an introduction to core syntactic concepts and representations. Part II, Grammatical Phenomena, reviews LFG work on a range of grammatical phenomena or constructions. Part III, Grammatical modules and interfaces, provides an overview of LFG work on semantics, argument structure, prosody, information structure, and morphology. Part IV, Linguistic disciplines, reviews LFG work in the disciplines of historical linguistics, learnability, psycholinguistics, and second language learning. Part V, Formal and computational issues and applications, provides an overview of computational and formal properties of the theory, implementations, and computational work on parsing, translation, grammar induction, and treebanks. Part VI, Language families and regions, reviews LFG work on languages spoken in particular geographical areas or in particular language families. The final section, Comparing LFG with other linguistic theories, discusses LFG work in relation to other theoretical approaches

Treebank-based acquisition of Chinese LFG resources for parsing and generation

This thesis describes a treebank-based approach to automatically acquire robust,wide-coverage Lexical-Functional Grammar (LFG) resources for Chinese parsing and generation, which is part of a larger project on the rapid construction of deep, large-scale, constraint-based, multilingual grammatical resources. I present an application-oriented LFG analysis for Chinese core linguistic phenomena and (in cooperation with PARC) develop a gold-standard dependency-bank of Chinese f-structures for evaluation. Based on the Penn Chinese Treebank, I design and implement two architectures for inducing Chinese LFG resources, one annotation-based and the other dependency conversion-based. I then apply the f-structure acquisition algorithm together with external, state-of-the-art parsers to parsing new text into "proto" f-structures. In order to convert "proto" f-structures into "proper" f-structures or deep dependencies, I present a novel Non-Local Dependency (NLD) recovery algorithm using subcategorisation frames and f-structure paths linking antecedents and traces in NLDs extracted from the automatically-built LFG f-structure treebank. Based on the grammars extracted from the f-structure annotated treebank, I develop a PCFG-based chart generator and a new n-gram based pure dependency generator to realise Chinese sentences from LFG f-structures. The work reported in this thesis is the first effort to scale treebank-based, probabilistic Chinese LFG resources from proof-of-concept research to unrestricted, real text. Although this thesis concentrates on Chinese and LFG, many of the methodologies, e.g. the acquisition of predicate-argument structures, NLD resolution and the PCFG- and dependency n-gram-based generation models, are largely language and formalism independent and should generalise to diverse languages as well as to labelled bilexical dependency representations other than LFG

Understanding misinformation on Twitter in the context of controversial issues

Social media is slowly supplementing, or even replacing, traditional media outlets such as television, newspapers, and radio. However, social media presents some drawbacks when it comes to circulating information. These drawbacks include spreading false information, rumors, and fake news. At least three main factors create these drawbacks: The filter bubble effect, misinformation, and information overload. These factors make gathering accurate and credible information online very challenging, which in turn may affect public trust in online information. These issues are even more challenging when the issue under discussion is a controversial topic. In this thesis, four main controversial topics are studied, each of which comes from a different domain. This variation of domains can give a broad view of how misinformation is manifested in social media, and how it is manifested differently in different domains. This thesis aims to understand misinformation in the context of controversial issue discussions. This can be done through understanding how misinformation is manifested in social media as well as by understanding people’s opinions towards these controversial issues. In this thesis, three different aspects of a tweet are studied. These aspects are 1) the user sharing the information, 2) the information source shared, and 3) whether specific linguistic cues can help in assessing the credibility of information on social media. Finally, the web application tool TweetChecker is used to allow online users to have a more in-depth understanding of the discussions about five different controversial health issues. The results and recommendations of this study can be used to build solutions for the problem of trustworthiness of user-generated content on different social media platforms, especially for controversial issues

Using Text Mining to Identify Crime Patterns from Arabic Crime News Report Corpus

Most text mining techniques have been proposed only for English text, and even here, most research has been conducted on specific texts related to special contexts within the English language, such as politics, medicine and crime. In contrast, although Arabic is a widely spoken language, few mining tools have been developed to process Arabic text, and some Arabic domains have not been studied at all. In fact, Arabic is a language with a very complex morphology because it is highly inflectional l, and therefore, dealing with texts written in Arabic is highly complicated. This research studies the crime domain in the Arabic language, exploiting unstructured text using text mining techniques. Developing a system for extracting important information from crime reports would be useful for police investigators, for accelerating the investigative process (instead of reading entire reports) as well as for conducting further or wider analyses. We propose the Crime Profiling System (CPS) to extract crime-related information (crime type, crime location and nationality of persons involved in the event), automatically construct dictionaries for the existing information, cluster crime documents based on certain attributes and utilize visualisation techniques to assist in crime data analysis. The proposed information extraction approach is novel, and it relies on computational linguistic techniques to identify the abovementioned information, i.e. without using predefined dictionaries (e.g. lists of location names) and annotated corpus. The language used in crime reporting is studied to identify patterns of interest using a corpus-based approach. Frequency analysis, collocation analysis and concordance analysis are used to perform the syntactic analysis in order to discover the local grammar. Moreover, the Self Organising Map (SOM) approach is adopted in order to perform the clustering and visualisation tasks for crime documents based on crime type, location or nationality. This clustering technique is improved because only refined data containing meaningful keywords extracted through the information extraction process are inputted into it, i.e. the data is cleaned by removing noise. As a result, a huge reduction in the quantity of data fed into the SOM is obtained, consequently, saving memory, data loading time and the execution time needed to perform the clustering. Therefore, the computation of the SOM is accelerated. Finally, the quantization error is reduced, which leads to high quality clustering. The outcome of the clustering stage is also visualised and the system is able to provide statistical information in the form of graphs and tables about crimes committed within certain periods of time and within a particular area.Saudi Cultural Burea