In recent years the interdisciplinary field of computational social science (CSS) has developed theory and research for developing spatial “agent-based” social simulation (SABSS) models of human societies situated in ecosystems that often comprise land cover and climate. This paper explains the needs and demand for GIS in these types of agent-based models, with an emphasis on models applied to ...

To evaluate document-to-document relevance is very important to many advanced applications such as IR, text mining and natural language processing. Since it is very hard to define document relevance in a mathematic way on account of users’ uncertainty, the concept of topical relevance is widely accepted by most of research fields. It suggests that a document relevance model should explain wheth...

In this paper, we compare the performance of four models in a retrieval based question answering dialogue task on two moderately sized corpora (~ 10,000 utterances). One model is statistical and uses cross language relevance while others are deep neural networks utilizing BERT architecture along with different methods. The has previously outperformed LSTM similar whereas been proven to perform ...

We formally derive a mathematical model for evaluating the effect of context relevance in language production. The model is based on the principle that distant contextual cues tend to gradually lose their relevance for predicting upcoming linguistic signals. We evaluate our model against a hypothesis of efficient communication (Genzel and Charniak’s Constant Entropy Rate hypothesis). We show th...

Language Models have been traditionally used in several fields like speech recognition or document retrieval. It was only recently when their use was extended to collaborative Recommender Systems. In this field, a Language Model is estimated for each user based on the probabilities of the items. A central issue in the estimation of such Language Model is smoothing, i.e., how to adjust the maxim...

The automated scoring of second-language (L2) learner text along various writing dimensions is an increasingly active research area. In this paper, we focus on determining the topical relevance of an essay to the prompt that elicited it. Given the burden involved in manually assigning scores for use in training supervised prompt-relevance models, we develop unsupervised models and show that the...