Understanding human's language requires complex world knowledge. However, existing
large-scale knowledge graphs mainly focus on knowledge about entities while ignoring …

Word embeddings are vectorial semantic representations built with either counting or
predicting techniques aimed at capturing shades of meaning from word co-occurrences …

Commonsense knowledge acquisition is a key problem for artificial intelligence.
Conventional methods of acquiring commonsense knowledge generally require laborious …

The Winograd Schema (WS) has been proposed as a test for measuring commonsense
capabilities of models. Recently, pre-trained language model-based approaches have …

Commonsense knowledge acquisition and reasoning have long been a core artificial
intelligence problem. However, in the past, there has been a lack of scalable methods to …

In this paper, we present the first comprehensive categorization of essential commonsense
knowledge for answering the Winograd Schema Challenge (WSC). For each of the …

Topic models are a popular tool for understanding text collections, but their evaluation has
been a point of contention. Automated evaluation metrics such as coherence are often used …

Distributional semantics develops theories and methods to represent the meaning of natural
language expressions, with vectors encoding their statistical distribution in linguistic …

Prior research has explored the ability of computational models to predict a word semantic fit
with a given predicate. While much work has been devoted to modeling the typicality relation …

This study aims to investigate and identify the driving factors of word co-occurrence from the
perspective of semantic relations between frequently co-occurring words. Natural sentences …