90 research outputs found
Source code and data for MWE'2011 papers
No full textContains the source code and data necessary to run all computations described in the following two papers: Finlayson, Mark A. and Kulkarni, Nidhi (2011) "Detecting Multi-Word Expressions improves Word Sense Disambiguation", in Proceedings of the 2011 Workshop on Multiword Expressions, held at ACL'2011 in Portland, OR; Kulkarni, Nidhi and Finlayson, Mark A. (2011) "jMWE: A Java Toolkit for Detecting Multi-Word Expressions" in Proceedings of the 2011 Workshop on Multiword Expressions, held at ACL'2011 in Portland, OR
Code for Java Libraries for Accessing the Princeton Wordnet: Comparison and Evaluation
No full textThis archive contains the code and data for running the evaluations described in: Finlayson, Mark Alan (2014) "Java Libraries for Accessing the Princeton Wordnet: comparison and Evaluation" in Proceedings of the 7th Global Wordnet Conference (GWC 2014). Tartu, Estonia, 25-29 January 2014. The archive contains five Eclipse projects (compatible with Eclipse 3.8.0) that may be imported directly into an Eclipse workspace. You will need a Java 1.4, 1.5, and 1.6 JRE to run all the code in the archive. Paper abstract: Java is a popular programming language for natural language processing. I compare and evaluate 12 Java libraries designed to access the information in the original Princeton Wordnet databases. From this comparison emerges a set of decision criteria that will enable a user to pick the library most suited to their purposes. I identify five deciding features: (1) availability of similarity metrics; (2) support for editing; (3) availability via Maven; (4) compatibility with retired Java versions; and (5) support for Enterprise Java. I also provide a comparison of other features of each library, the information exposed by each API, and the versions of Wordnet each library supports, and I evaluate each library for the speed of various retrieval operations. In the case that the user's application does not require one of the deciding features, I show that my library, JWI, the MIT Java Wordnet Interface, is the highest-performance, widest-coverage, easiest-to-use library available
Development of a scintillating reference grid for spatial-phase-locked electron-beam lithography
No full textThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 59-61).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.by Mark Alan Finlayson.S.M
Supplementary Materials for "A Survey of Corpora in Computational and Cognitive Narrative Science"
No full textThis archive contains supplementary materials for the article titled "A Survey of Corpora in Computational and Cognitive Narrative Science" by Mark A. Finlayson, published in the journal *Sprache und Datenverarbeitung*. The archive contains two files. The first file is the raw bibliographic data of the survey, containing 2600+ citations. The second file is a spreadsheet with the coded features of each corpus, plus the analyses that underlie sections 3 & 4 of the paper
Learning narrative structure from annotated folktales
No full textThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 97-100).Narrative structure is an ubiquitous and intriguing phenomenon. By virtue of structure we recognize the presence of Villainy or Revenge in a story, even if that word is not actually present in the text. Narrative structure is an anvil for forging new artificial intelligence and machine learning techniques, and is a window into abstraction and conceptual learning as well as into culture and its in influence on cognition. I advance our understanding of narrative structure by describing Analogical Story Merging (ASM), a new machine learning algorithm that can extract culturally-relevant plot patterns from sets of folktales. I demonstrate that ASM can learn a substantive portion of Vladimir Propp's in influential theory of the structure of folktale plots. The challenge was to take descriptions at one semantic level, namely, an event timeline as described in folktales, and abstract to the next higher level: structures such as Villainy, Stuggle- Victory, and Reward. ASM is based on Bayesian Model Merging, a technique for learning regular grammars. I demonstrate that, despite ASM's large search space, a carefully-tuned prior allows the algorithm to converge, and furthermore it reproduces Propp's categories with a chance-adjusted Rand index of 0.511 to 0.714. Three important categories are identied with F-measures above 0.8. The data are 15 Russian folktales, comprising 18,862 words, a subset of Propp's original tales. This subset was annotated for 18 aspects of meaning by 12 annotators using the Story Workbench, a general text-annotation tool I developed for this work. Each aspect was doubly-annotated and adjudicated at inter-annotator F-measures that cluster around 0.7 to 0.8. It is the largest, most deeply-annotated narrative corpus assembled to date. The work has significance far beyond folktales. First, it points the way toward important applications in many domains, including information retrieval, persuasion and negotiation, natural language understanding and generation, and computational creativity. Second, abstraction from natural language semantics is a skill that underlies many cognitive tasks, and so this work provides insight into those processes. Finally, the work opens the door to a computational understanding of cultural in influences on cognition and understanding cultural differences as captured in stories.by Mark Alan Finlayson.Ph.D
Sets of Signals, Information Flow, and Folktales
No full textI apply Barwise and Seligman’s theory of information flow to understand how sets of signals can carry information. More precisely I focus on the case where the information of interest is not present in any individual signal, but rather is carried by correlations between signals. This focus has the virtue of highlighting an oft-neglected process, viz., the different methods that apply categories to raw signals. Different methods result in different information, and the set of available methods provides a way of characterizing relative degrees of intensionality. I illustrate my points with the case of folktales and how they transmit cultural information. Certain sorts of cultural information, such as a grammar of hero stories, are not found in any individual tale but rather in a set of tales. Taken together, these considerations lead to some comments regarding the “information unit” of narratives and other complex signals.This material is based on work supported by the U.S. Office of Naval Research, Grant No. N00014-09-1-0597. Any opinions, findings, conclusions or recommendations therein are those of the author(s) and do not necessarily reflect the views of the Office of Naval Research
Annotation Guide for the UCM/MIT Indications, Referential Expressions, and Coreference Corpus (UMIREC Corpus)
No full textThis is the annotation guide given to the annotators who created the UCM/MIT Indications, Referring Expressions, and Coreference (UMIREC) Corpus version 1.0. The corpus comprises texts annotated for referring expressions, coreference relations between the referring expressions, and so-called "indication structures", which split referring expressions into constituents (nuclei and modifiers) and mark each constituent as either 'distinctive' or 'descriptive', which indicate whether or not the constituent contains information required for uniquely identifying the referent. The contents of this corpus, the annotation procedure, and the indication structures are described in more detail in a paper titled "The Prevalence of Descriptive Referring Expressions in News and Narrative" published in the proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, held in July 2010 in Uppsala, Sweden (ACL-2010)
The Story Workbench: An Extensible Semi-Automatic Text Annotation Tool
No full textText annotations are of great use to researchers in the language sciences, and much effort has been invested in creating annotated corpora for an wide variety of purposes. Unfortunately, software support for these corpora tends to be quite limited: it is usually ad-hoc, poorly designed and documented, or not released for public use. I describe an annotation tool, the Story Workbench, which provides a generic platform for text annotation. It is free, open-source, cross-platform, and user friendly. It provides a number of common text annotation operations, including representations (e.g., tokens, sentences, parts of speech), functions (e.g., generation of initial annotations by algorithm, checking annotation validity by rule, fully manual manipulation of annotations) and tools (e.g., distributing texts to annotators via version control, merging doubly-annotated texts into a single file). The tool is extensible at many different levels, admitting new representations, algorithm, and tools. I enumerate ten important features and illustrate how they support the annotation process at three levels: (1) annotation of individual texts by a single annotator, (2) double-annotation of texts by two annotators and an adjudicator, and (3) annotation scheme development. The Story Workbench is scheduled for public release in March 2012
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