We present in this paper a study on negation in dialogues. In particular, we analyze the peculiarities of negation in dialogues and propose a new method to detect intra-sentential and inter-sentential negation scope and focus in dialogue context. A key element of the solution is to use dialogue context in the form of previous utterances, which is often needed for proper interpretation of negation in dialogue compared to literary, non-dialogue texts. We have modeled the negation scope and focus detection tasks as a sequence labeling tasks and used Conditional Random Field models to label each token in an utterance as being within the scope/focus of negation or not. The proposed negation scope and focus detection method is evaluated on a newly created corpus (called the DeepTutor Negation corpus; DT-Neg). This dataset was created from actual tutorial dialogue interactions between high school students and a state-of-the-art intelligent tutoring system.

Vector based word representation models are often developed from very large corpora. However, we often encounter words in real world applications that are not available in a single vector model. In this paper, we present a novel Neural Network (NN) based approach for obtaining representations for words in a target model from another model, called the source model, where representations for the words are available, effectively pooling together their vocabularies. Our experiments show that the transformed vectors are well correlated with the native target model representations and that an extrinsic evaluation based on a word-to-word similarity task using the Simlex-999 dataset leads to results close to those obtained using native model representations.

We present in this paper an innovative solution to the challenge of building effective educational technologies that offer tailored instruction to each individual learner. The proposed solution in the form of a conversational intelligent tutoring system, called DeepTutor, has been developed as a web application that is accessible 24/7 through a browser from any device connected to the Internet. The success of several large scale experiments with high-school students using DeepTutor is a solid proof that conversational intelligent tutoring at scale over the web is possible.

This paper shows that the combination of knowledge and corpus-based word-to-word similarity measures can produce higher agreement with human judgment than any of the in-dividual measures. While this might be a predictable result, the paper provides insights about the circumstances under which a combination is productive and about the improve-ment levels that are to be expected. The experiments presented here were conducted using the word-to-word similarity measures included in SEMILAR, a freely available semantic similarity toolkit.

We study and propose in this article several novel solutions to the task of semantic similarity between two short texts. The proposed solutions are based on the probabilistic method of Latent Dirichlet Allocation (LDA) and on the algebraic method of Latent Semantic Analysis (LSA). Both methods, LDA and LSA, are completely automated methods used to discover latent topics or concepts from large collection of documents. We propose a novel word-to-word similarity measure based on LDA as well as several text-to-text similarity measures. We compare these measures with similar, known measures based on LSA. Experiments and results are presented on two data sets: the Microsoft Research Paraphrase corpus and the User Language Paraphrase corpus. We found that the novel word-to-word similarity measure based on LDA is extremely promising.