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Suppose we want to build a system that answers a natural language question by representing its
semantics as a logical form and computing the answer given a structured database of facts. The
core par...
Learning Entailment Relations by Global Graph Structure Optimization
Structure Optimization Entailment Relations
2015/9/10
Identifying entailment relations between predicates is an important part of applied semantic
inference. In this article we propose a global inference algorithm that learns such entailment
rules. Fir...
Corpus-based Learning of Analogies and Semantic Relations
analogy metaphor semantic relations Vector Space Model cosine similarity noun-modifier pairs
2015/7/30
We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college e...
A learning theory should try to answer the following questions. What does it mean to learn? How is something learnt? How is the learnt information stored, processed and ultimately translated into the ...
Using Speakers’ Referential Intentions to Model Early Cross-Situational Word Learning
word meaning
2015/6/24
Word learning is a ‘‘chicken and egg’’ problem. If a child could understand speakers’ utterances, it
would be easy to learn the meanings of individual words,
and once a child knows what many words m...
Learning hierarchical category structure in deep neural networks
neural networks hierarchical generative models semantic cognition learning dynamics
2015/6/23
Psychological experiments have revealed remarkable regularities in the developmental time course of cognition. Infants generally acquire broad categorical distinctions (i.e., plant/animal) before fine...
Learning and applying contextual constraints in sentence comprehension
Learning applying contextual sentence comprehension
2015/6/19
Learning and applying contextual constraints in sentence comprehension.
Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide...
Robust textual inference via learning and abductive reasoning
textual inference learning and abductive reasoning
2015/6/12
We present a system for textual inference (the task of inferring whether a sentence follows from another text) that uses learning and a logical-formula semantic representation of the text. More precis...
Despite much recent progress on accurate semantic role labeling, previous work has largely used independent classifiers,possibly combined with separate label sequence models via Viterbi decoding. This...
This paper proposes a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering a...
Learning to recognize features of valid textual entailments
recognize features valid textual entailments
2015/6/12
This paper advocates a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering ...
Learning Alignments and Leveraging Natural Logic
Learning Alignments Leveraging Natural Logic
2015/6/12
We describe an approach to textual inference that improves alignments at both the typed dependency level and at a deeper semantic level. We present a machine learning approach to alignment scoring, a ...
How semantic biases in simple adjacencies affect learning a complex structure with non-adjacencies in AGL: a statistical account
language acquisition semantic biases statistical learning
2015/4/21
A major theoretical debate in language acquisition research regards the learnability of hierarchical structures. The artificial grammar learning methodology is increasingly influential in approaching ...
Learning Document-Level Semantic Properties from Free-text Annotations
Free-text Annotations Semantic Properties
2014/11/26
Learning Document-Level Semacntic Properties from Free-text Annotations。