8,105 research outputs found
The Latent Relation Mapping Engine: Algorithm and Experiments
Many AI researchers and cognitive scientists have argued that analogy is the
core of cognition. The most influential work on computational modeling of
analogy-making is Structure Mapping Theory (SMT) and its implementation in the
Structure Mapping Engine (SME). A limitation of SME is the requirement for
complex hand-coded representations. We introduce the Latent Relation Mapping
Engine (LRME), which combines ideas from SME and Latent Relational Analysis
(LRA) in order to remove the requirement for hand-coded representations. LRME
builds analogical mappings between lists of words, using a large corpus of raw
text to automatically discover the semantic relations among the words. We
evaluate LRME on a set of twenty analogical mapping problems, ten based on
scientific analogies and ten based on common metaphors. LRME achieves
human-level performance on the twenty problems. We compare LRME with a variety
of alternative approaches and find that they are not able to reach the same
level of performance.Comment: related work available at http://purl.org/peter.turney
Principles of Acquaintance
The thesis that in order to genuinely think about a particular object one must be (in some sense) acquainted with that object has been thoroughly explored since it was put forward by Bertrand Russell. Recently, the thesis has come in for mounting criticism. The aim of this paper is to point out that neither the exploration nor the criticism have been sensitive to the fact that the thesis can be interpreted in two different ways, yielding two different principles of acquaintance. One principle uses the notion of content in distinguishing genuine thinking-about things from a merely derivative kind of thinking-about things. The other principle is quiet about content, focusing instead on a distinction between satisfactional and non-satisfactional means of bringing things into thought. Most work has focused on the first, content-based principle of acquaintance. But criticisms of this principle do not apply straightforwardly to the non-content-based principle. I shall argue that the latter principle merits independent assessment as part of the broader effort to account for genuine thinking about particular objects. In the final section of the paper, I will sketch a roadmap for this assessment
Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
Retrieving 3D models from 2D human sketches has received considerable
attention in the areas of graphics, image retrieval, and computer vision.
Almost always in state of the art approaches a large amount of "best views" are
computed for 3D models, with the hope that the query sketch matches one of
these 2D projections of 3D models using predefined features.
We argue that this two stage approach (view selection -- matching) is
pragmatic but also problematic because the "best views" are subjective and
ambiguous, which makes the matching inputs obscure. This imprecise nature of
matching further makes it challenging to choose features manually. Instead of
relying on the elusive concept of "best views" and the hand-crafted features,
we propose to define our views using a minimalism approach and learn features
for both sketches and views. Specifically, we drastically reduce the number of
views to only two predefined directions for the whole dataset. Then, we learn
two Siamese Convolutional Neural Networks (CNNs), one for the views and one for
the sketches. The loss function is defined on the within-domain as well as the
cross-domain similarities. Our experiments on three benchmark datasets
demonstrate that our method is significantly better than state of the art
approaches, and outperforms them in all conventional metrics.Comment: CVPR 201
Uvid u automatsko izluÄivanje metaforiÄkih kolokacija
Collocations have been the subject of much scientific research over the years. The focus of this research is on a subset of collocations, namely metaphorical collocations. In metaphorical collocations, a semantic shift has taken place in one of the components, i.e., one of the components takes on a transferred meaning. The main goal of this paper is to review the existing literature and provide a systematic overview of the existing research on collocation extraction, as well as the overview of existing methods, measures, and resources. The existing research is classified according to the approach (statistical, hybrid, and distributional semantics) and presented in three separate sections. The insights gained from existing research serve as a first step in exploring the possibility of developing a method for automatic extraction of metaphorical collocations. The methods, tools, and resources that may prove useful for future work are highlighted.Kolokacije su veÄ dugi niz godina tema mnogih znanstvenih istraživanja. U fokusu ovoga istraživanja podskupina je kolokacija koju Äine metaforiÄke kolokacije. Kod metaforiÄkih je kolokacija kod jedne od sastavnica doÅ”lo do semantiÄkoga pomaka, tj. jedna od sastavnica poprima preneseno znaÄenje. Glavni su ciljevi ovoga rada istražiti postojeÄu literaturu te dati sustavan pregled postojeÄih istraživanja na temu izluÄivanja kolokacija i postojeÄih metoda, mjera i resursa. PostojeÄa istraživanja opisana su i klasificirana prema razliÄitim pristupima (statistiÄki, hibridni i zasnovani na distribucijskoj semantici). TakoÄer su opisane razliÄite asocijativne mjere i postojeÄi naÄini procjene rezultata automatskoga izluÄivanja kolokacija. Metode, alati i resursi koji su koriÅ”teni u prethodnim istraživanjima, a mogli bi biti korisni za naÅ” buduÄi rad posebno su istaknuti. SteÄeni uvidi u postojeÄa istraživanja Äine prvi korak u razmatranju moguÄnosti razvijanja postupka za automatsko izluÄivanje metaforiÄkih kolokacija
Computational Linguistics and Natural Language Processing
This chapter provides an introduction to computational linguistics methods,
with focus on their applications to the practice and study of translation. It
covers computational models, methods and tools for collection, storage,
indexing and analysis of linguistic data in the context of translation, and
discusses the main methodological issues and challenges in this field. While an
exhaustive review of existing computational linguistics methods and tools is
beyond the scope of this chapter, we describe the most representative
approaches, and illustrate them with descriptions of typical applications.Comment: This is the unedited author's copy of a text which appeared as a
chapter in "The Routledge Handbook of Translation and Methodology'', edited
by F Zanettin and C Rundle (2022
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