Browsing by Author "Diab, Mona"
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Item Measuring Verb Similarity(2000-06-21) Resnik, Philip; Diab, MonaThe way we model semantic similarity is closely tied to our understanding of linguistic representations. We present several models of semantic similarity, based on differing representational assumptions, and investigate their properties via comparison with human ratings of verb similarity. The results offer insight into the bases for human similarity judgments and provide a testbed for further investigation of the interactions among syn tactic properties, semantic structure, and semantic con tent. (Also cross-referenced as UMIACS-TR-2000-40, LAMP-TR-047)Item A Preliminary Statistical Investigation into the impact of an N-Gram Analysis Approach based on Word Syntactic Categories toward Text Author Classification(2000-06-17) Diab, Mona; Schuster, John; Bock, PeterQuantitative analysis of literary style has heretofore utilized semantic elements-word counts. This research attempts to identify quantifiable syntactic elements of style that can be used for author identification. The measurement of syntactic elements utilizes a dictionary with one part of speech per word and looks at phrases delimited by punctuation marks. Different size permutations of words - referred to as grams - are counted within each text. Correlations are measured amongst the gram frequencies of eight texts pertaining to four authors, both contemporary and non-contemporary. The correlations are performed across different gram sizes of words. The same treatment is applied to a target text, the Funeral Elegy text. The approach holds for classifying texts temporally consistently across the various gram sizes. Yet a finer grained investigation is required to certify the authorship of the Funeral Elegy text. (Also cross-referenced as UMIACS-TR-2000-39, LAMP-TR-046)Item Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing(Association for Computational Linguistics, 2012-07-13) Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona; Dorr, Bonnie; Levin, Lori; Piatko, Christine; Rambow, Owen; Van Durme, BenjaminWe explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for the overwhelming majority of sentences. We investigate an approach to automatically training a modality tagger where we first gathered sentences based on a high-recall simple rule-based modality tagger and then provided these sentences to Mechanical Turk annotators for further annotation. We used the resulting set of training data to train a precise modality tagger using a multi-class SVM that delivers good performance.Item A Statistical Word-Level Translation Model for Comparable Corpora(2000-06-17) Diab, Mona; Finch, SteveIn this paper, we present a model of statistical word-level mapping for comparable corpora. The approach is based on the assumption that if two terms have close distributional profiles, their corresponding translations' distributional profiles should be close in a comparable corpus. The proposed model is described. A preliminary investigation on intralanguage comparable corpora is laid out. The preliminary results are >92% accurate, suggesting the feasibility of the model. The model needs to undergo some improvements and should be tested cross linguistically before assessing its significance. (Also cross-referenced as UMIACS-TR-2000-41, LAMP-TR-048)