4,022 research outputs found
Heuristic Ranking in Tightly Coupled Probabilistic Description Logics
The Semantic Web effort has steadily been gaining traction in the recent
years. In particular,Web search companies are recently realizing that their
products need to evolve towards having richer semantic search capabilities.
Description logics (DLs) have been adopted as the formal underpinnings for
Semantic Web languages used in describing ontologies. Reasoning under
uncertainty has recently taken a leading role in this arena, given the nature
of data found on theWeb. In this paper, we present a probabilistic extension of
the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks
(MLNs) as probabilistic semantics. This extension is tightly coupled, meaning
that probabilistic annotations in formulas can refer to objects in the
ontology. We show that, even though the tightly coupled nature of our language
means that many basic operations are data-intractable, we can leverage a
sublanguage of MLNs that allows to rank the atomic consequences of an ontology
relative to their probability values (called ranking queries) even when these
values are not fully computed. We present an anytime algorithm to answer
ranking queries, and provide an upper bound on the error that it incurs, as
well as a criterion to decide when results are guaranteed to be correct.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty
in Artificial Intelligence (UAI2012
More cat than cute? Interpretable Prediction of Adjective-Noun Pairs
The increasing availability of affect-rich multimedia resources has bolstered
interest in understanding sentiment and emotions in and from visual content.
Adjective-noun pairs (ANP) are a popular mid-level semantic construct for
capturing affect via visually detectable concepts such as "cute dog" or
"beautiful landscape". Current state-of-the-art methods approach ANP prediction
by considering each of these compound concepts as individual tokens, ignoring
the underlying relationships in ANPs. This work aims at disentangling the
contributions of the `adjectives' and `nouns' in the visual prediction of ANPs.
Two specialised classifiers, one trained for detecting adjectives and another
for nouns, are fused to predict 553 different ANPs. The resulting ANP
prediction model is more interpretable as it allows us to study contributions
of the adjective and noun components. Source code and models are available at
https://imatge-upc.github.io/affective-2017-musa2/ .Comment: Oral paper at ACM Multimedia 2017 Workshop on Multimodal
Understanding of Social, Affective and Subjective Attributes (MUSA2
Computing the Affective-Aesthetic Potential of Literary Texts
In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (>150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSM’s ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 > 0.5, p < 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p < 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results
Graph Interpolation Grammars: a Rule-based Approach to the Incremental Parsing of Natural Languages
Graph Interpolation Grammars are a declarative formalism with an operational
semantics. Their goal is to emulate salient features of the human parser, and
notably incrementality. The parsing process defined by GIGs incrementally
builds a syntactic representation of a sentence as each successive lexeme is
read. A GIG rule specifies a set of parse configurations that trigger its
application and an operation to perform on a matching configuration. Rules are
partly context-sensitive; furthermore, they are reversible, meaning that their
operations can be undone, which allows the parsing process to be
nondeterministic. These two factors confer enough expressive power to the
formalism for parsing natural languages.Comment: 41 pages, Postscript onl
Acquisition of aspect and aktionsart by children in croatian and french
Our results indicate some differences in the use of aspect between French and Croatian speaking children. In Croatian language children always manage to keep the appropriate aspect, unlike French children. However, the imperfective aspect seems to be better acquired in French children than the perfective aspect. The perfective aspect, the marked form both in French as well as in Croatian, is related to the lexical meaning of the verbs. The acquisition of the Aktionsart in both languages seems to be more a matter of semantics than of morphology. Furthermore, our data suggest the existence of a specific developmental trend in the use of Aktionsart (intensive, iterative and inchoative), which is similar for children speaking Slavic and Romanic languages
Language, logic and ontology: uncovering the structure of commonsense knowledge
The purpose of this paper is twofold: (i) we argue that the structure of commonsense knowledge must be discovered, rather than invented; and (ii) we argue that natural
language, which is the best known theory of our (shared) commonsense knowledge, should itself be used as a guide to discovering the structure of commonsense knowledge. In addition to suggesting a systematic method to the discovery of the structure of commonsense knowledge, the method we propose seems to also provide an explanation for a number of phenomena in natural language, such as metaphor, intensionality, and the semantics of nominal compounds. Admittedly, our ultimate goal is quite ambitious, and it is no less than the systematic ‘discovery’ of a well-typed
ontology of commonsense knowledge, and the subsequent formulation of the longawaited goal of a meaning algebra
Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMs
In our opinion the exuberance surrounding the relative success of data-driven
large language models (LLMs) is slightly misguided and for several reasons (i)
LLMs cannot be relied upon for factual information since for LLMs all ingested
text (factual or non-factual) was created equal; (ii) due to their subsymbolic
na-ture, whatever 'knowledge' these models acquire about language will always
be buried in billions of microfeatures (weights), none of which is meaningful
on its own; and (iii) LLMs will often fail to make the correct inferences in
several linguistic contexts (e.g., nominal compounds, copredication, quantifier
scope ambi-guities, intensional contexts. Since we believe the relative success
of data-driven large language models (LLMs) is not a reflection on the symbolic
vs. subsymbol-ic debate but a reflection on applying the successful strategy of
a bottom-up reverse engineering of language at scale, we suggest in this paper
applying the effective bottom-up strategy in a symbolic setting resulting in
symbolic, explainable, and ontologically grounded language models.Comment: 17 page
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