Exhaustive pairing errors in passives

Children make exhaustive pairing (EP) errors with universal quantifiers, rejecting sentences like (1) for Figure 1 because of the extra object, in contrast to adults. EP errors have been explained as a syntactic, semantic and/or pragmatic issue (Philip, 2011), or caused by experimental artifacts (Crain et-al., 1996). Children made more EP errors with quantified objects (Kang, 2001). When the quantified set was introduced as discourse topic, however, EP errors disappeared (Drozd & van Loosbroek, 2006). This only happened though for quantified subjects, not quantified objects (Hollebrandse, 2004). To further examine the role of grammatical function, we investigated passives. We expected that children might perform worse on passives than actives, because passivization disrupts the canonical mapping between thematic roles and grammatical function

Paraphrasing Using Given and New Information in a Question-Answer System

The design and implementation of a paraphrase component for a natural language question-answer system (CO-OP) is presented. A major point made is the role of given and new information in formulating a paraphrase that differs in a meaningful way from the user\u27s question. A description is also given of the transformational grammar used by the paraphraser to generate questions

Neurobiology of incremental speech comprehension

Understanding spoken language requires the rapid transition from perceptual processing of the auditory input through a variety of cognitive processes involved in constructing the mental representation of the message that the speaker is intending to convey. Listeners carry out these complex processes very rapidly and accurately as they hear each word incrementally unfolding in a sentence. However, little is known about the specific spatiotemporal patterning of this wide range of incremental processing operations that underpin the dynamic transitions from the speech input to the development of a meaning interpretation of an utterance. This thesis aims to address this set of issues by investigating the spatiotemporal dynamics of brain activity as spoken sentences unfold over time in order to illuminate the neurocomputational properties of the human language processing system and determine how the representation of a spoken sentence develops incrementally as each upcoming word is heard. Using a novel application of multidimensional probabilistic modelling combined with models from computational linguistics, I developed models of a variety of computational processes associated with accessing and processing the syntactic and semantic properties of sentences and tested these models at various points as sentences unfolded over time. Since a wide range of incremental processes occur very rapidly during speech comprehension, it is crucial to keep track of the temporal dynamics of the neural computations involved. To do this, I used combined electroencephalography and magnetoencephalography (EMEG) to record neural activity with millisecond resolution and analyzed the recordings in source space using univariate and/or multivariate approaches. The results confirm the value of this combination of methods in examining the properties of incremental speech processing. My findings corroborate the predictive nature of human speech comprehension and demonstrate that the effects of early semantic constraint are not dependent on explicit syntactic knowledge

Two set-theoretic approaches to the semantics of adjective-noun combinations

This work addresses the problem of adjective-noun combinations. Conventionally, adjectives belong to a hierarchy. This has the consequence that a uniform treatment of adjectives is unattainable---without resorting to notions such as possible worlds, which are difficult to map into competent computer programs. In this work, we propose two set-theoretic approaches to the semantics of adjective-noun combinations. The first hypothesizes that an adjective-noun compound is a subset of its constituent noun. The second hypothesizes that the adjective-noun combinations can semantically be thought of as a set intersection involving the adjective(s) and the head noun of the compound. This work argues that the class of adjectives known as privative can be accommodated within an existing class in the adjective hierarchy, known as subsective . This step is important for the provision of uniform treatments of adjective-noun combinations. The two approaches make use of types, both for gaining a finer granularity of analysis and for imposing structure on the problem domain. It is shown that the mixture of a typing system with set theory provides promising results that are manifested in the provision of compositional solutions to the adjective-noun combinations. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .A24. Source: Masters Abstracts International, Volume: 43-01, page: 0229. Adviser: Richard Frost. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Examining Semantic Effects in Conceptual Combination

Conceptual combination is a cognitive process that produces complex concepts (e.g., adjective-noun pairs) from simple concepts. The Selective Modification Model (SMM; Smith, Osherson, Rips, & Keane, 1988) postulates that simple adjective-noun combinations (e.g., red apple) are understood by the modifier red selecting the colour attribute of the head noun apple. Theories of conceptual combination have not extended to fulfill our understanding of how complex adjective-noun pairs (e.g., empty dream) are processed. This exploratory study had two main objectives: to determine which semantic variables best captured the processing of complex adjective-noun pairs and to examine the semantic effects of conceptual combination to extend current theories. Adjective-noun combinations were manipulated based on subjective ratings (i.e., concreteness and plausibility; see the preliminary study) or objective measures (i.e., age of acquisition and semantic distance) and compared. Two hundred and ninety-three participants were randomly assigned to complete one of three computerized tasks that differentially engaged semantic processing from shallow to deep, including the non-pronounceable double lexical decision task (Experiment 1), the pronounceable double lexical decision task (Experiment 2), and the meaningfulness task (Experiment 3). Across all tasks, the subjective model outperformed the objective model in reaction time and accuracy analyses. Adjective-noun processing was facilitated by concrete, early acquired head nouns, as well as adjective-noun pairs that were rated as plausible and situated close in semantic space. Interestingly, adjectives paired with abstract head nouns were difficult to process across tasks regardless of how plausible the pair was. In conclusion, semantic variables rated by participants are valuable and may better capture how the mental lexicon is organized and accessed, and further research should pursue innovative ways of examining how abstract head nouns are processed to incorporate into existing theories

The Language of Dialogue Is Complex

Integrative Complexity (IC) is a psychometric that measures the ability of a person to recognize multiple perspectives and connect them, thus identifying paths for conflict resolution. IC has been linked to a wide variety of political, social and personal outcomes but evaluating it is a time-consuming process requiring skilled professionals to manually score texts, a fact which accounts for the limited exploration of IC at scale on social media.We combine natural language processing and machine learning to train an IC classification model that achieves state-of-the-art performance on unseen data and more closely adheres to the established structure of the IC coding process than previous automated approaches. When applied to the content of 400k+ comments from online fora about depression and knowledge exchange, our model was capable of replicating key findings of prior work, thus providing the first example of using IC tools for large-scale social media analytics.Comment: 12 pages, 9 figures, 10 table