Sentiment analysis is one of the traditional well-known tasks in Natural Language
Processing (NLP) research. In recent years, Pre-trained Models (PMs) have become one of …

We propose a simple approach to combining first-order logic and probabilistic graphical
models in a single representation. A Markov logic network (MLN) is a first-order knowledge …

This thesis deals with gradience in grammar, ie, with the fact that some linguistic structures
are not fully acceptable or unacceptable, but receive gradient linguistic judgments. The …

Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling
tasks like shallow parsing (Sha and Pereira, 2003) and namedentity extraction (McCallum …

This paper develops a declarative language, P-log, that combines logical and probabilistic
arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while …

We propose a logical/mathematical framework for statistical parameter learning of
parameterized logic programs, ie definite clause programs containing probabilistic facts with …

Weighted finite-state transducers suffer from the lack of a training algorithm. Training is even
harder for transducers that have been assembled via finite-state operations such as …

Interest in statistical relational learning (SRL) has grown rapidly in recent years. Several key
SRL tasks have been identified, and a large number of approaches have been proposed …

Probabilistic analogues of regular and context-free grammars are well-known in
computational linguistics, and currently the subject of intensive research. To date, however …

Stochastic logic programs (SLPs) are logic programs with parameterised clauses which
define a log-linear distribution over refutations of goals. The log-linear distribution provides …