Using Ellipsis Detection and Word Similarity for Transformation of Spoken Language into Grammatically Valid Sentences

When humans speak they often use gram-matically incorrect sentences, which is a problem for grammar-based language pro-cessing methods, since they expect in-put that is valid for the grammar. We present two methods to transform spoken language into grammatically correct sen-tences. The first is an algorithm for au-tomatic ellipsis detection, which finds el-lipses in spoken sentences and searches in a combinatory categorial grammar for suitable words to fill the ellipses. The sec-ond method is an algorithm that computes the semantic similarity of two words us-ing WordNet, which we use to find alter-natives to words that are unknown to the grammar. In an evaluation, we show that the usage of these two methods leads to an increase of 38.64 % more parseable sen-tences on a test set of spoken sentences that were collected during a human-robot interaction experiment.

Measuring Semantic Textual Similarity and Automatic Answer Assessment in Dialogue Based Tutoring Systems

This dissertation presents methods and resources proposed to improve onmeasuring semantic textual similarity and their applications in student responseunderstanding in dialogue based Intelligent Tutoring Systems. In order to predict the extent of similarity between given pair of sentences,we have proposed machine learning models using dozens of features, such as thescores calculated using optimal multi-level alignment, vector based compositionalsemantics, and machine translation evaluation methods. Furthermore, we haveproposed models towards adding an interpretation layer on top of similaritymeasurement systems. Our models on predicting and interpreting the semanticsimilarity have been the top performing systems in SemEval (a premier venue for thesemantic evaluation) for the last three years. The correlations between our models\u27predictions and the human judgments were above 0.80 for several datasets while ourmodels being very robust than many other top performing systems. Moreover, wehave proposed Bayesian. We have also proposed a novel Neural Network based word representationmapping approach which allows us to map the vector based representation of a wordfound in one model to the another model where the word representation is missing,effectively pooling together the vocabularies and corresponding representationsacross models. Our experiments show that the model coverage increased by few toseveral times depending on which model\u27s vocabulary is taken as a reference. Also,the transformed representations were well correlated to the native target modelvectors showing that the mapped representations can be used with condence tosubstitute the missing word representations in the target model. models to adapt similarity models across domains. Furthermore, we have proposed methods to improve open-ended answersassessment in dialogue based tutoring systems which is very challenging because ofthe variations in student answers which often are not self contained and need thecontextual information (e.g., dialogue history) in order to better assess theircorrectness. In that, we have proposed Probabilistic Soft Logic (PSL) modelsaugmenting semantic similarity information with other knowledge. To detect intra- and inter-sentential negation scope and focus in tutorialdialogs, we have developed Conditional Random Fields (CRF) models. The resultsindicate that our approach is very effective in detecting negation scope and focus intutorial dialogue context and can be further developed to augment the naturallanguage understanding systems. Additionally, we created resources (datasets, models, and tools) for fosteringresearch in semantic similarity and student response understanding inconversational tutoring systems

The predictive nature of language comprehension

This dissertation explores the hypothesis that predictive processing--the access and construction of internal representations in advance of the external input that supports them--plays a central role in language comprehension. Linguistic input is frequently noisy, variable, and rapid, but it is also subject to numerous constraints. Predictive processing could be a particularly useful approach in language comprehension, as predictions based on the constraints imposed by the prior context could allow computation to be speeded and noisy input to be disambiguated. Decades of previous research have demonstrated that the broader sentence context has an effect on how new input is processed, but less progress has been made in determining the mechanisms underlying such contextual effects. This dissertation is aimed at advancing this second goal, by using both behavioral and neurophysiological methods to motivate predictive or top-down interpretations of contextual effects and to test particular hypotheses about the nature of the predictive mechanisms in question. The first part of the dissertation focuses on the lexical-semantic predictions made possible by word and sentence contexts. MEG and fMRI experiments, in conjunction with a meta-analysis of the previous neuroimaging literature, support the claim that an ERP effect classically observed in response to contextual manipulations--the N400 effect--reflects facilitation in processing due to lexical-semantic predictions, and that these predictions are realized at least in part through top-down changes in activity in left posterior middle temporal cortex, the cortical region thought to represent lexical-semantic information in long-term memory,. The second part of the dissertation focuses on syntactic predictions. ERP and reaction time data suggest that the syntactic requirements of the prior context impacts processing of the current input very early, and that predicting the syntactic position in which the requirements can be fulfilled may allow the processor to avoid a retrieval mechanism that is prone to similarity-based interference errors. In sum, the results described here are consistent with the hypothesis that a significant amount of language comprehension takes place in advance of the external input, and suggest future avenues of investigation towards understanding the mechanisms that make this possible

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

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

The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study

Carminati MN, Knoeferle P. The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study. Presented at the Architectures and Mechanisms of Language and Processing (AMLaP), Riva del Garda, Italy

Application of Resolution Rules on phi-Features in L2 Compositions: Native Arabic Writers in an L2 English

Resolution rules are syntactic parameters that regulate the proper agreement of phi-features (person, number, and gender) between a noun/noun phrase and a verb phrase within a grammatical language system. This study examines L2 English compositions written by native Arabic speakers and investigates whether or not students transfer agreement patterns from their L1 to their L2. Although the compositions were examined primarily for salient resolution rule agreement errors, the scope was widened to also include other agreement issues that were prevalent. The findings revealed that although agreement errors were found in conjunction with person and number resolution rules, these were not the most wide-spread agreement errors with this group of Arabic writers. Constructions that included isolated subject referents and indefinite pronouns proved more difficult to resolve, and negative transfer led to copious zero copula errors and pro-drop errors. In terms of subject/verb agreement for these writers, data from this study determined that auxiliary verb constructions were the most difficult