Deana Pennell

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This paper explores several unsupervised approaches to automatic keyword extraction using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) weighting framework, we incorporated partof-speech (POS) information, word clustering, and sentence salience score. We also evaluated a graph-based approach that measures the importance of a(More)
This paper describes a two-phase method for expanding abbreviations found in informal text (e.g., email, text messages, chat room conversations) using a machine translation system trained at the character level during the first phase. In this way, the system learns mappings between character-level “phrases” and is much more robust to new abbreviations than(More)
This paper describes a normalization system for text messages to allow them to be read by a TTS engine. To address the large number of texting abbreviations, we use a statistical classifier to learn when to delete a character. The features we use are based on character context, function, and position in the word and containing syllable. To ensure that our(More)
This paper describes a text normalization system for deletion-based abbreviations in informal text. We propose using statistical classifiers to learn the probability of deleting a given character using features based on character context, position in the word and containing syllable, and function within the word. To ensure that our system is robust to(More)
Keywords play a vital role in extracting the correct information as per user requirements. Keywords are like index terms that contain the most important information about the content of the document. Keyword Extraction is the task of identifying a keyword or keyphrase from a document that can help users easily to understand the documents. Meeting(More)
This work reports the benefits of Statistical Machine Translation (SMT) in template messaging domain. SMT has become an actual and practical technology due to significant increment in both the computational power and storage capacity of computers and the availability of large volumes of bilingual data. Through SMT a sentences written with misspelled words,(More)
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