This dissertation uses digital humanities methods and computational linguistic tools to perform a sentiment analysis on a corpus of 18 th century Moravian English personal memoirs. This historical corpus contains important similarities to modern natural language text, requiring …
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This dissertation demonstrates a framework for incremental model selection and processing of highly variant speech transcripts and user-generated text. The system reduces natural language processing (NLP) ambiguity by segmenting text by domain, allowing for domain-specific downs…
Language plays a very important role in understanding the culture and mindset of people. Given the abundance of electronic multilingual data, it is interesting to see what insight can be gained by automatic analysis of text. This in turn calls for text analysis which is focused …
Human annotators are critical for creating the necessary datasets to train statistical learners, but annotation cost and limited access to qualified annotators forms a data bottleneck. In recent years, researchers have investigated overcoming this obstacle using crowdsourcing, w…
The main archive of life sciences literature currently contains more than 18,000,000 references, and it is virtually impossible for any human to stay up-to-date with this large number of papers, even in a specific sub-domain. Not every fact that is reported in the literature is …
The shortage of Arabic language resources in the field of corpus linguistics compared to other popular languages such as English, Chinese and Spanish inspired this work. The research in the field of dialectal Arabic is still limited due to the relative unavailability of resource…
The Arabic language is a morphologically complex language that causes various difficulties for various NLP systems, such as POS tagging. The motive of this research is to investigate the development and training of a compression-based Arabic POS tagger using the PPM algorithm. T…
This dissertation presents original techniques for a class of problems that can be collectively referred to as relationship extraction. This machine learning task involves extracting tuples from free text, the exemplar instantiations of which help model the target relationship. …
Cross Language Information Retrieval (CLIR) deals with the problem of retrieving documents written in a language different from that of the query. The popularization of the Internet has enabled access to less known languages such as Arabic, and made the CLIR with Arabic language…
Cross-document entity co-reference resolution task is part of and largely based on many Natural Language Processing (NLP) innovations that have been developed in the past several decades and are still evolving. At the core of the task is determining whether two or more pieces of…
Automatic translation between natural languages using empirical learning methods is an active research area. Much of the work in this area is concentrated on acquiring models of translation from expensive resources, including large databases containing hundreds of thousands of t…
Most of the world's natural languages have complex morphology. But the expense of building morphological analyzers by hand has prevented the development of morphological analysis systems for the large majority of languages. Unsupervised induction techniques, that learn from unan…
The goal for this project is to explore strategies in adapting a Part of Speech (POS) tagger that was trained on Modern Standard Arabic sentences for tagging Levantine sentences, a dialect of Modern Standard Arabic, leveraged by methods of morphological analysis. I propose a tag…
In this thesis we investigate applications of natural language processing (NLP) techniques to information security problems. We present our results in this direction for two important areas: password authentication, and information hiding in natural language text. We have limite…
A language model (LM) is a probability distribution over all possible word sequences. It is a vital component of many natural language processing tasks, such as automatic speech recognition, statistical machine translation, information retrieval and so on. The art of language mo…
Social media provide a democratized platform for expressing one's opinion or viewpoint. The everyday discussions that people have with their families, friends and colleagues became available through blogging services. The emergence of the blogging activity made the classic ethno…
Quantifier scope disambiguation (QSD) is one of the most challenging problems in deep natural language understanding (NLU) systems. The most popular approach for dealing with QSD is to simply leave the semantic representation (scope-) underspecified and to incrementally add cons…
This dissertation describes an approach to automatically detecting and correcting grammatical errors in text produced by Korean language learners. Specifically, we focus on Korean particles, which have a range of functions including case marking and indicate properties similar t…
Natural Language Processing has become more and more popular with the recent advances in Artificial Intelligence. Fundamental improvements have been introduced in word representations to store semantic and/or syntactic features. With the recently published language model BERT, c…