Distributional, corpus-based descriptions have frequently been applied to model aspects of word meaning. However, distributional models that use corpus data as their basis have one well-known disadvantage: Even though the distributional features based on corpus co-occurrence were often successful in capturing meaning aspects of the words to be described, they generally fail to capture those mea...

We combine logical and distributional representations of natural language meaning by transforming distributional similarity judgments into weighted inference rules using Markov Logic Networks (MLNs). We show that this framework supports both judging sentence similarity and recognizing textual entailment by appropriately adapting the MLN implementation of logical connectives. We also show that d...

The term Distributional semantic models (DSMs) refers to a family of unsupervised corpus-based approaches to semantic similarity computation. These models rely on the distributional hypothesis (Harris, 1954), which states that semantically related words tend to share many of their contexts. So, by collecting information about the contexts in which words are used in a corpus, DSMs are able to me...

Basin hydrologic response is a function of soil moisture distributional featuresAn information-based dimensionless index of hydrologic complexity is appliedThe complexity index characterizes soil moisture distributional features.

This thesis is about the problem of compositionality in distributional semantics. Distributional semantics presupposes that the meanings of words are a function of their occurrences in textual contexts. It models words as distributions over these contexts and represents them as vectors in high dimensional spaces. The problem of compositionality for such models concerns itself with how to produc...

We predict the compositionality of multiword expressions using distributional similarity between each component word and the overall expression, based on translations into multiple languages. We evaluate the method over English noun compounds, English verb particle constructions and German noun compounds. We show that the estimation of compositionality is improved when using translations into m...

This paper develops a minimally supervised approach, based on focused distributional similarity methods and discourse connectives, for identifying of causality relations between events in context. While it has been shown that distributional similarity can help identifying causality, we observe that discourse connectives and the particular discourse relation they evoke in context provide additio...

By using Bismut’s approach to the Malliavin calculus with jumps, we study the regularity of the distributional density for SDEs driven by degenerate additive Lévy noises. Under full Hörmander’s conditions, we prove the existence of distributional density and the weak continuity in the first variable of the distributional density. Moreover, under a uniform first order Lie’s bracket condition, we...

Probabilistic Soft Logic (PSL) is a recently developed framework for probabilistic logic. We use PSL to combine logical and distributional representations of natural-language meaning, where distributional information is represented in the form of weighted inference rules. We apply this framework to the task of Semantic Textual Similarity (STS) (i.e. judging the semantic similarity of naturallan...

The task of automatically acquiring semantically related words have led people to study distributional similarity. The distributional hypothesis states that words that are similar share similar contexts. In this paper we present a technique that aims at improving the performance of a syntax-based distributional method by augmenting the original input of the system (syntactic co-occurrences) wit...