Browsing by Subject "Natural language processing (Computer science)"

Now showing 1 - 5 of 5
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    Building a Linguistics based Loss Function for Dialogue Generation
    (2020) St. Clair, Jack; Chandlee, Jane
    This paper will investigate different loss functions used for various natural language processing (NLP) machine learning tasks. These loss functions have proven their worth in the area of machine translation but they have been shown to be inadequate for the task of dialogue generation. Thus, this paper proposes some potential additions to these loss functions that add more linguistic information with the goal of improving dialogue generation to get machine learning algorithms closer to creating human like dialogue.
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    Grapheme to Phoneme Conversion: Using Input Strictly Local Finite State Transducers
    (2019) Morgan, Gregory M.; Chandlee, Jane
    This thesis explores the many methods of Grapheme to Phoneme Conversion (G2P) including dictionary look-up, rule-based approaches, and probabilistic approaches such as Joint Sequence Models (JSM), Recurrent Neural Networks (RNN), and weighted finite state automata (WFST) as well as a discussion of letter to phoneme alignments methods. We then explain Strictly Local languages and functions and their previous applications in an Input Strictly Local FST Learning Algorithm. Finally, I propose a further application for G2P conversion by adapting the Input Strictly Local FST Learning Algorithm. My results indicate that while this algorithm had some success learning G2P, future work will be necessary to improve accuracy by implementing a probabilistic model.
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    Natural Language Interaction with Robots
    (2007) Walker, Alden; Dougherty, John P.; Kumar, Deepak
    Natural language communication with robots has obvious uses in almost all areas of life. Computer-based natural language interaction is an active area of research in Computational Linguistics and AI. While there have been several NL systems built for specific computer applications, NL interaction with robots remains largely unexplored. Our research focuses on implementing a natural language interpreter for commands and queries given to a small mobile robot. Our goal is to implement a complete system for natural language understanding in this domain, and as such consists of two main parts: a system for parsing the subset of English our robot is to understand and a semantic analyzer used to extract meaning from the natural language. By using such a system we will be able to demonstrate that a mobile robot is capable of understanding NL commands and queries and responding to them appropriately.
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    Natural Language Processing and Translation using Augmented Transition Networks and Semantic Networks
    (2003) Ramos, Juan; Kumar, Deepak
    The problem of computers understanding and communicating with humans using natural languages such as English is a complicated task with many details to examine and explore. The goal of this project, then, is to examine some of the established data structures and methods used to enable computers to understand and generate natural language. In an attempt to contribute some original material, we will also consider how a computer might be able to translate sentences between English and Spanish. The techniques covered in this paper are well-established data structures and methods for parsing and generating natural language sentences. In particular, we will pay close attention to the augmented transition network model (ATN) and semantic networks. The ATN data structure is a powerful mechanism for interpreting natural language constructs, most notably due to its ability to both parse and generate language with a single network. Extending the ATN structure slightly will also allow for our goal of language translation. The semantic network model will assist in this endeavor by representing the input data as a network of entity nodes connected by labeled arcs that represent the relationship between nodes. This model abstracts the input into a form independent from the source and target languages, facilitating the task of translation immensely. Finally, we will provide a demonstration of how SNePS, a LISP-based system that incorporates ATNs and semantic networks, translates a simple set of sentences using the techniques described.
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    Sentiment Analysis of Egyptian Arabic in Social Media
    (2014) Abdalkader, Mohamed; Kumar, Deepak; Darwish, Manar
    Sentiment analysis is an emerging area of application fueled by the increase of public participation in online social media. Much work has been done on sentiment analysis in English while less work has been done on other languages like Mandarin and Arabic. Arabic is spoken by hundreds of millions of people in over twenty countries. Modern Standard Arabic (MSA) is used online mostly by newspapers and other official sources. However, social media and blogs used by individuals are typically in Dialect Arabic (DA). My Senior Thesis work has been focused on exploring ways to increase the accuracy of automated sentiment analysis in Egyptian Arabic through using the specific features of Arabic. I found that the baseline algorithm makes the most mistakes in classifying tweets that carry a sentiment as neutral tweets. Using Minimum Edit Distance (MED) and ISRI Arabic stemmer, I was able to decrease the error of the baseline algorithm by 31% without having to add any new entries to the lexicon. My approach has allowed me to not only get over the challenge of different morphological forms but also misspelling and informal writing. While I cannot empirically compare it to results by other authors as I am using a different data set, my approach reaches an accuracy of 78% which has an improvement of 14.7% over the baseline.