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- 13Natural Language Processing
- 4Artificial Intelligence
- 4Machine Learning
- 2Question Answering
- 2Reinforcement Learning
Answer typing is an important aspect of the question answering process. Most commonly addressed with the use of a fixed set of possible answer classes via question classification, answer typing influences which answers will ultimately be selected as correct. Answer typing introduces the concept...
Algorithmic decipherment is a prime example of a truly unsupervised problem. This thesis presents several algorithms developed for the purpose of decrypting unknown alphabetic scripts representing unknown languages. We assume that symbols in scripts which contain no more than a few dozen unique...
The conversion of romanized texts back to the native scripts is a challenging task because of the inconsistent romanization conventions and non-standard language use. This problem is compounded by code-mixing, i.e., using words from more than one language within the same discourse. Considering...
This work is concerned with the problem of extracting structured information networks from a text corpus. The nodes of the network are recognizable entities, typically people, locations, or organizations, while the edges denote relations among such entities. We use state-of-the-art natural...
Introduction: We describe an approach to parsing and logical translation that was inspired by Gazdar's work on context-free grammar for English. Each grammar rule consists of a syntactic part that specifies an acceptable fragment of a parse tree, and a semantic part that specifies how the logical...
Word sense disambiguation (WSD) is one of the core tasks in natural language processing and its objective is to identify the sense of a content word (nouns, verbs, adjectives, and adverbs) in context, given a predefined sense inventory. Although WSD is a monolingual task, it has been conjectured...
Inflectional morphology presents numerous problems for traditional computational models, not least of which is an increase in the number of rare types in any corpus. Although few annotated corpora exist for morphologically complex languages, it is possible for lay-speakers of the language to...
Morphologically complex languages such as Arabic pose several challenges in Natural Language Processing (NLP) due to their complexity and token sparsity. Most techniques approach the problem by transforming the words of the language from their sparse surface form representation to a less sparse...
It has gotten increasingly harder for laypersons to determine the veracity of online health information. This is because of the explosion of content in health social media, allowing anyone with an Internet connection to create and propagate health-related content. This includes both innocuous and...
Language Modeling (LM) is often formulated as a next-word prediction problem over a large vocabulary, which makes it challenging. To effectively perform the task of next-word prediction, Long Short Term Memory networks (LSTMs) must keep track of many types of information. Some information is...