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Towards a Unified Model of Language Acquisition
In this theoretical paper, we first review and rebut standard criticisms against distributional approaches to language acquisition. We then present two closely-related models that use distributional analysis. The first deals with the acquisition of vocabulary, the second with grammatical development. We show how these two models can be combined with a semantic network grown using Hebbian learning, and briefly illustrate the advantages of this combination. An important feature of this hybrid system is that it combines two different types of distributional learning, the first based on order, and the second based on co-occurrences within a context
On the Feasibility of Automated Detection of Allusive Text Reuse
The detection of allusive text reuse is particularly challenging due to the
sparse evidence on which allusive references rely---commonly based on none or
very few shared words. Arguably, lexical semantics can be resorted to since
uncovering semantic relations between words has the potential to increase the
support underlying the allusion and alleviate the lexical sparsity. A further
obstacle is the lack of evaluation benchmark corpora, largely due to the highly
interpretative character of the annotation process. In the present paper, we
aim to elucidate the feasibility of automated allusion detection. We approach
the matter from an Information Retrieval perspective in which referencing texts
act as queries and referenced texts as relevant documents to be retrieved, and
estimate the difficulty of benchmark corpus compilation by a novel
inter-annotator agreement study on query segmentation. Furthermore, we
investigate to what extent the integration of lexical semantic information
derived from distributional models and ontologies can aid retrieving cases of
allusive reuse. The results show that (i) despite low agreement scores, using
manual queries considerably improves retrieval performance with respect to a
windowing approach, and that (ii) retrieval performance can be moderately
boosted with distributional semantics
A Unified multilingual semantic representation of concepts
Semantic representation lies at the core of several applications in Natural Language Processing. However, most existing semantic representation techniques cannot be used effectively for the representation of individual word senses. We put forward a novel multilingual concept representation, called MUFFIN , which not only enables accurate representation of word senses in different languages, but also provides multiple advantages over existing approaches. MUFFIN represents a given concept in a unified semantic space irrespective of the language of interest, enabling cross-lingual comparison of different concepts. We evaluate our approach in two different evaluation benchmarks, semantic similarity and Word Sense Disambiguation, reporting state-of-the-art performance on several standard datasets
Learning to distinguish hypernyms and co-hyponyms
This work is concerned with distinguishing different semantic relations which exist between distributionally similar words. We compare a novel approach based on training a linear Support Vector Machine on pairs of feature vectors with state-of-the-art methods based on distributional similarity. We show that the new supervised approach does better even when there is minimal information about the target words in the training data, giving a 15% reduction in error rate over unsupervised approaches
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