Grounding Meaning: Composition versus Abstraction

I recently read an interesting paper, How Is Meaning Grounded in Dictionary Definitions? The abstract follows:
Meaning cannot be based on dictionary definitions all the way down: at some point the circularity of definitions must be broken in some way, by grounding the meanings of certain words in sensorimotor categories learned from experience or shaped by [...]

The Logic of Attributional and Relational Similarity

In a previous post, I discussed the distinction between attributes and relations:
An attribute is a characteristic of an entity, whereas a relation is a connection between two or more entities. In logic, we can define an attribute as a predicate with one argument and a relation as a predicate with two or more arguments. The [...]

Why Computational Linguistics?

I was thinking about what to say to a student who is contemplating a career in computational linguistics. How can I convey my enthusiasm? How can I explain my fascination with language? Here are some of the things that came to mind:
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AI Success Stories

I’ve been invited to give a talk on AI Success Stories, so I’ve compiled a list of things that illustrate progress in AI research. By success, for the purpose of this talk, I mean something that is interesting and impressive to a wide audience, rather than something that is successful in terms of commercial or [...]

How to Maximize Citations

The Seven Secrets of Highly Cited Scientists
A couple of years ago, I discussed with some colleagues the topic of maximizing citations for academic research papers. Here is a summary of the discussion.
Why should we want our papers to be highly cited? I assume here that we want our work to influence other researchers, and [...]

Three Levels of Thought

Peter Gärdenfors proposes that there are three levels of abstraction for modeling thought:

Symbolic: logic, expert systems, Prolog, Cyc, good old-fashioned AI, theorem proving
Spatial: geometry, feature spaces, conceptual spaces, semantic spaces, information retrieval, vector space models, latent semantic analysis, machine learning
Connectionist: neural networks, Hebbian theory, associationism, perceptrons, neuroscience

These levels might be compared to modeling physics at [...]

SVD and Tucker Decomposition with Low RAM Requirements

Recently I’ve been experimenting with algorithms for the Singular Value Decomposition and the Tucker Decomposition, with the goal of processing large matrices (more than 105 rows and columns) and large tensors (more than 104 rows, columns, and tubes) that are relatively sparse (about 10% density). The problem with matrices and tensors of this size is [...]

Tensors for Data and Text Analysis

For the last several months, I’ve been playing with tensors as an approach to data and text analysis. Here are some pointers to get started on tensors.
Tensors are a generalization of matrices to higher dimensions:

order 0 tensor = scalar
order 1 tensor = vector
order 2 tensor = matrix
order n > 2 tensor = higher order tensor

PARAFAC [...]

The Symbol Grounding Problem

There is a view that the meaning of words (more generally, of symbols) must be grounded in sensory perception or in physical interaction with the world (embodiment). If symbols were merely defined in terms of other symbols, then it seems that we would have an infinite regression; we would spin in circles in symbol space, [...]

Lexicons versus Corpora for Measures of Semantic Distance

Measures of semantic distance (or, inversely, semantic relatedness) have many applications in Computational Linguistics. There are three basic approaches to measuring semantic distance: lexicon-based algorithms, corpus-based algorithms, and hybrids. In an otherwise excellent paper on lexicon-based measures, Budanitsky and Hirst criticize corpus-based measures. I discuss their criticisms here.
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