Linguistic Ambiguity in Language-based Jokes

The purpose of this study was to (1) identify patterns in joke type, word class, word class progressions, use of morphologic/syllabic mechanisms, and compound word manipulations in the “serious” and “humorous” interpretations of puns, (2) compare results with two previous studies (Attardo et al. 1994b and Bucaria 2004) and delineate discrepancies, and (3) to explore how language pattern(s) in English puns contribute to our theoretical understanding of linguistic interpretation. From a collection of 6,000 puns published online, 225 were randomly chosen and analyzed for alliterative, phonological, lexical, and syntactic categorizations, as well as for patterns in word class, word class progressions, use of morphologic/syllabic mechanisms, and compound word manipulations. Results indicate a high use of syllabic and morphologic mechanisms in the formation of language-based jokes…a phenomenon which has previously been unexplored. It also found a proportionally low use of adverbs despite their standing as open class words. Finally, this study found a consistent trend across all linguistic levels for holistic processing. New standards for marginal joke type categorizations are proposed based on syllabic and morphological characteristics. In addition, lack of adverbial use is attributed to proximity, transitivity, similarity, and mobility of this particular word class. Discrepancies between authors\u27 results are attributed to genre, joke elimination, and differing standards for categorization. Finally, holistic processing is discussed from the theoretical perspective of the Sapir-Whorf Hypothesis

A Similarity Based Concordance Approach to Word Sense Disambiguation

This study attempts to solve the problem of Word Sense Disambiguation using a combination of statistical, probabilistic and word matching algorithms. These algorithms consider that words and sentences have some hidden similarities and that the polysemous words in any context should be assigned to a sense after each execution of the algorithm. The algorithm was tested with sufficient sample data and the efficiency of the disambiguation performance has proven to increase significantly after the inclusion of the concordance methodology

A Similarity Based Concordance Approach to Word Sense Disambiguation

This study attempts to solve the problem of Word Sense Disambiguation using a combination of statistical, probabilistic and word matching algorithms. These algorithms consider that words and sentences have some hidden similarities and that the polysemous words in any context should be assigned to a sense after each execution of the algorithm. The algorithm was tested with sufficient sample data and the efficiency of the disambiguation performance has proven to increase significantly after the inclusion of the concordance methodology

Tagging and parsing with cascaded Markov models : automation of corpus annotation

This thesis presents new techniques for parsing natural language. They are based on Markov Models, which are commonly used in part-of-speech tagging for sequential processing on the world level. We show that Markov Models can be successfully applied to other levels of syntactic processing. first two classification task are handled: the assignment of grammatical functions and the labeling of non-terminal nodes. Then, Markov Models are used to recognize hierarchical syntactic structures. Each layer of a structure is represented by a separate Markov Model. The output of a lower layer is passed as input to a higher layer, hence the name: Cascaded Markov Models. Instead of simple symbols, the states emit partial context-free structures. The new techniques are applied to corpus annotation and partial parsing and are evaluated using corpora of different languages and domains.Ausgehend von Markov-Modellen, die für das Part-of-Speech-Tagging eingesetzt werden, stellt diese Arbeit Verfahren vor, die Markov-Modelle auch auf weiteren Ebenen der syntaktischen Verarbeitung erfolgreich nutzen. Dies betrifft zum einen Klassifikationen wie die Zuweisung grammatischer Funktionen und die Bestimmung von Kategorien nichtterminaler Knoten, zum anderen die Zuweisung hierarchischer, syntaktischer Strukturen durch Markov-Modelle. Letzteres geschieht durch die Repräsentation jeder Ebene einer syntaktischen Struktur durch ein eigenes Markov-Modell, was den Namen des Verfahrens prägt: Kaskadierte Markov-Modelle. Deren Zustände geben anstelle atomarer Symbole partielle kontextfreie Strukturen aus. Diese Verfahren kommen in der Korpusannotation und dem partiellen Parsing zum Einsatz und werden anhand mehrerer Korpora evaluiert

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

Semantic Priming Effects in Lexical Ambiguity Resolution

The study addresses a number of issues related to the effects of biasing semantic contexts on the processing of words with more than one meaning (homographs). Biasing contexts have been taken to either constrain “lexical access” to a contextually relevant meaning of a homograph (selective access), or to exert a selective effect only after access to all, or some subset of, the meanings of a homograph (multiple access). Recent findings based on the two-factor theory of attention (Posner & Snyder, 1975a) suggest that lexical access occurs in two stages, where the first stage involves automatic activation of all meanings and the second involves a rapid attentional selection of the contextually relevant meaning. A three word priming paradigm (Schvaneveldt, Meyer, & Becker, 1976) was employed to test the stages hypothesis. Subjects were required to name only the final target word, and their reaction time was the dependent variable. The critical trials involved presentation of the two word primes, where the first prime was a word related to one meaning of the second prime, which was a homograph. The comparison of most interest was between targets that were semantically congruent or incongruent with the biased homograph (e.g., oar-row-PADDLE and oar-row-COLUMN, respectively). These conditions were compared to two baselines: One employing two neutral primes (e.g., xxxxx-xxxxx-PADDLE), and one employing the biased homograph followed by an unrelated target (e.g., oar-row-GREEN). The stimulus onset asynchtrony (SOA) of the homograph was varied, as well as the strategies that subjects were instructed to use in attending to the context stimuli. Some evidence was found for the stages view of ambiguity resolution: At brief SOAs, congruent and incongruent targets were facilitated, whereas at a longer SOA, facilitation was significantly reduced for incongruent targets. Attentional strategies had less effect than anticipated. Also, results with the neutral baseline were discrepant with earlier findings. Discussion focused on the research hypotheses and characteristics of the naming task that might account for the discrepant findings. A brief theoretical overview concluded

Learning Grammars for Architecture-Specific Facade Parsing

International audienceParsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four diff erent datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images from Paris following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework

Structured Access in Sentence Comprehension

This thesis is concerned with the nature of memory access during the construction of long-distance dependencies in online sentence comprehension. In recent years, an intense focus on the computational challenges posed by long-distance dependencies has proven to be illuminating with respect to the characteristics of the architecture of the human sentence processor, suggesting a tight link between general memory access procedures and sentence processing routines (Lewis & Vasishth 2005; Lewis, Vasishth, & Van Dyke 2006; Wagers, Lau & Phillips 2009). The present thesis builds upon this line of research, and its primary aim is to motivate and defend the hypothesis that the parser accesses linguistic memory in an essentially structured fashion for certain long-distance dependencies. In order to make this case, I focus on the processing of reflexive and agreement dependencies, and ask whether or not non-structural information such as morphological features are used to gate memory access during syntactic comprehension. Evidence from eight experiments in a range of methodologies in English and Chinese is brought to bear on this question, providing arguments from interference effects and time-course effects that primarily syntactic information is used to access linguistic memory in the construction of certain long-distance dependencies. The experimental evidence for structured access is compatible with a variety of architectural assumptions about the parser, and I present one implementation of this idea in a parser based on the ACT-R memory architecture. In the context of such a content-addressable model of memory, the claim of structured access is equivalent to the claim that only syntactic cues are used to query memory. I argue that structured access reflects an optimal parsing strategy in the context of a noisy, interference-prone cognitive architecture: abstract structural cues are favored over lexical feature cues for certain structural dependencies in order to minimize memory interference in online processing