56,285 research outputs found
Incremental Models of Natural Language Category Acquisition
Learning categories from examples is a fundamental problem faced by the human cognitive system, and a long-standing topic of investigation in psychology. In this work we focus on the acquisition of natural language categories and examine how the statistics of the linguistic environment influence category formation. We present two incremental models of category acquisition — one probabilistic, one graph-based — which encode different assumptions about how concepts are represented (i.e., as a set of topics or nodes in a graph). Evaluation against gold-standard clusters and human performance in a category acquisition task suggests that the graph-based approach is better suited at modeling the acquisition of natural language categories
Modelling the acquisition of natural language categories
The ability to reason about categories and category membership is fundamental to human
cognition, and as a result a considerable amount of research has explored the acquisition
and modelling of categorical structure from a variety of perspectives. These
range from feature norming studies involving adult participants (McRae et al. 2005) to
long-term infant behavioural studies (Bornstein and Mash 2010) to modelling experiments
involving artificial stimuli (Quinn 1987).
In this thesis we focus on the task of natural language categorisation, modelling
the cognitively plausible acquisition of semantic categories for nouns based on purely
linguistic input. Focusing on natural language categories and linguistic input allows us
to make use of the tools of distributional semantics to create high-quality representations
of meaning in a fully unsupervised fashion, a property not commonly seen in traditional
studies of categorisation. We explore how natural language categories can be
represented using distributional models of semantics; we construct concept representations
for corpora and evaluate their performance against psychological representations
based on human-produced features, and show that distributional models can provide a
high-quality substitute for equivalent feature representations.
Having shown that corpus-based concept representations can be used to model category
structure, we turn our focus to the task of modelling category acquisition and
exploring how category structure evolves over time. We identify two key properties
necessary for cognitive plausibility in a model of category acquisition, incrementality
and non-parametricity, and construct a pair of models designed around these constraints.
Both models are based on a graphical representation of semantics in which
a category represents a densely connected subgraph. The first model identifies such
subgraphs and uses these to extract a flat organisation of concepts into categories; the
second uses a generative approach to identify implicit hierarchical structure and extract
an hierarchical category organisation. We compare both models against existing
methods of identifying category structure in corpora, and find that they outperform
their counterparts on a variety of tasks. Furthermore, the incremental nature of our
models allows us to predict the structure of categories during formation and thus to
more accurately model category acquisition, a task to which batch-trained exemplar
and prototype models are poorly suited
Predicting and Explaining Human Semantic Search in a Cognitive Model
Recent work has attempted to characterize the structure of semantic memory
and the search algorithms which, together, best approximate human patterns of
search revealed in a semantic fluency task. There are a number of models that
seek to capture semantic search processes over networks, but they vary in the
cognitive plausibility of their implementation. Existing work has also
neglected to consider the constraints that the incremental process of language
acquisition must place on the structure of semantic memory. Here we present a
model that incrementally updates a semantic network, with limited computational
steps, and replicates many patterns found in human semantic fluency using a
simple random walk. We also perform thorough analyses showing that a
combination of both structural and semantic features are correlated with human
performance patterns.Comment: To appear in proceedings for CMCL 201
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
In this paper, we describe a so-called screening approach for learning robust
processing of spontaneously spoken language. A screening approach is a flat
analysis which uses shallow sequences of category representations for analyzing
an utterance at various syntactic, semantic and dialog levels. Rather than
using a deeply structured symbolic analysis, we use a flat connectionist
analysis. This screening approach aims at supporting speech and language
processing by using (1) data-driven learning and (2) robustness of
connectionist networks. In order to test this approach, we have developed the
SCREEN system which is based on this new robust, learned and flat analysis.
In this paper, we focus on a detailed description of SCREEN's architecture,
the flat syntactic and semantic analysis, the interaction with a speech
recognizer, and a detailed evaluation analysis of the robustness under the
influence of noisy or incomplete input. The main result of this paper is that
flat representations allow more robust processing of spontaneous spoken
language than deeply structured representations. In particular, we show how the
fault-tolerance and learning capability of connectionist networks can support a
flat analysis for providing more robust spoken-language processing within an
overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial
Intelligence Research 6(1), 199
The impact of adjacent-dependencies and staged-input on the learnability of center-embedded hierarchical structures
A theoretical debate in artificial grammar learning (AGL) regards the learnability of hierarchical structures. Recent studies using an AnBn grammar draw conflicting conclusions (Bahlmann and Friederici, 2006, De Vries et al., 2008). We argue that 2 conditions crucially affect learning AnBn structures: sufficient exposure to zero-level-of-embedding (0-LoE) exemplars and a staged-input. In 2 AGL experiments, learning was observed only when the training set was staged and contained 0-LoE exemplars. Our results might help understanding how natural complex structures are learned from exemplars
A distributional model of semantic context effects in lexical processinga
One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›
A role for the developing lexicon in phonetic category acquisition
Infants segment words from fluent speech during the same period when they are learning phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. Simulations demonstrate that word-level information can successfully disambiguate overlapping English vowel categories. Learning patterns in the model are shown to parallel human behavior from artificial language learning tasks. These findings point to a central role for the developing lexicon in phonetic category acquisition and provide a framework for incorporating top-down constraints into models of category learning
- …