70 Matching Results

Search Results

Advanced search parameters have been applied.

Networks and Natural Language Processing

Description: Article discussing networks and natural language processing. The authors present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.
Date: September 2008
Creator: Radev, Dragomir R. & Mihalcea, Rada, 1974-
Partner: UNT College of Engineering

SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Embodiment Challenge

Description: This paper presents the results of the Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge, aiming to bring together researchers in educational NLP technology and textual entailment.
Date: June 2013
Creator: Dzikovska, Myroslava O.; Nielsen, Rodney D.; Brew, Chris; Leacock, Claudia; Giampiccolo, Danilo; Bentivogli, Luisa et al.
Partner: UNT College of Engineering

STREAMLInED Challenges: Aligning Research Interests with Shared Tasks

Description: This paper describes the use of Shared Task Evaluation Campaigns by designing tasks that are compelling to speech and natural language processing researchers while addressing technical challenges in language documentation and exploiting growing archives of endangered language data.
Date: March 2017
Creator: Levow, Gina-Anne; Bender, Emily M.; Littell, Patrick; Howell, Kristen; Chelliah, Shobhana Lakshmi; Crowgey, Joshua et al.
Partner: UNT College of Information

Extrapolating Subjectivity Research to Other Languages

Description: Socrates articulated it best, "Speak, so I may see you." Indeed, language represents an invisible probe into the mind. It is the medium through which we express our deepest thoughts, our aspirations, our views, our feelings, our inner reality. From the beginning of artificial intelligence, researchers have sought to impart human like understanding to machines. As much of our language represents a form of self expression, capturing thoughts, beliefs, evaluations, opinions, and emotions which are not available for scrutiny by an outside observer, in the field of natural language, research involving these aspects has crystallized under the name of subjectivity and sentiment analysis. While subjectivity classification labels text as either subjective or objective, sentiment classification further divides subjective text into either positive, negative or neutral. In this thesis, I investigate techniques of generating tools and resources for subjectivity analysis that do not rely on an existing natural language processing infrastructure in a given language. This constraint is motivated by the fact that the vast majority of human languages are scarce from an electronic point of view: they lack basic tools such as part-of-speech taggers, parsers, or basic resources such as electronic text, annotated corpora or lexica. This severely limits the implementation of techniques on par with those developed for English, and by applying methods that are lighter in the usage of text processing infrastructure, we are able to conduct multilingual subjectivity research in these languages as well. Since my aim is also to minimize the amount of manual work required to develop lexica or corpora in these languages, the techniques proposed employ a lever approach, where English often acts as the donor language (the fulcrum in a lever) and allows through a relatively minimal amount of effort to establish preliminary subjectivity research in a target language.
Date: May 2013
Creator: Banea, Carmen
Partner: UNT Libraries

Machine Language Techniques for Conversational Agents

Description: Machine Learning is the ability of a machine to perform better at a given task, using its previous experience. Various algorithms like decision trees, Bayesian learning, artificial neural networks and instance-based learning algorithms are used widely in machine learning systems. Current applications of machine learning include credit card fraud detection, customer service based on history of purchased products, games and many more. The application of machine learning techniques to natural language processing (NLP) has increased tremendously in recent years. Examples are handwriting recognition and speech recognition. The problem we tackle in this Problem in Lieu of Thesis is applying machine-learning techniques to improve the performance of a conversational agent. The OpenMind repository of common sense, in the form of question-answer pairs is treated as the training data for the machine learning system. WordNet is interfaced with to capture important semantic and syntactic information about the words in the sentences. Further, k-closest neighbors algorithm, an instance based learning algorithm is used to simulate a case based learning system. The resulting system is expected to be able to answer new queries with knowledge gained from the training data it was fed with.
Date: December 2003
Creator: Sule, Manisha D.
Partner: UNT Libraries