100 research outputs found
Word Sense Disambiguation with LSTM: Do We Really Need 100 Billion Words?
Recently, Yuan et al. (2016) have shown the effectiveness of using Long
Short-Term Memory (LSTM) for performing Word Sense Disambiguation (WSD). Their
proposed technique outperformed the previous state-of-the-art with several
benchmarks, but neither the training data nor the source code was released.
This paper presents the results of a reproduction study of this technique using
only openly available datasets (GigaWord, SemCore, OMSTI) and software
(TensorFlow). From them, it emerged that state-of-the-art results can be
obtained with much less data than hinted by Yuan et al. All code and trained
models are made freely available
Statistical mechanics of the spherical hierarchical model with random fields
We study analytically the equilibrium properties of the spherical
hierarchical model in the presence of random fields. The expression for the
critical line separating a paramagnetic from a ferromagnetic phase is derived.
The critical exponents characterising this phase transition are computed
analytically and compared with those of the corresponding -dimensional
short-range model, leading to conclude that the usual mapping between one
dimensional long-range models and -dimensional short-range models holds
exactly for this system, in contrast to models with Ising spins. Moreover, the
critical exponents of the pure model and those of the random field model
satisfy a relationship that mimics the dimensional reduction rule. The absence
of a spin-glass phase is strongly supported by the local stability analysis of
the replica symmetric saddle-point as well as by an independent computation of
the free-energy using a renormalization-like approach. This latter result
enlarges the class of random field models for which the spin-glass phase has
been recently ruled out.Comment: 23 pages, 2 figure
Checking Chase Termination over Ontologies of Existential Rules with Equality
The chase is a sound and complete algorithm for conjunctive query answering
over ontologies of existential rules with equality. To enable its effective
use, we can apply acyclicity notions; that is, sufficient conditions that
guarantee chase termination. Unfortunately, most of these notions have only
been defined for existential rule sets without equality. A proposed solution to
circumvent this issue is to treat equality as an ordinary predicate with an
explicit axiomatisation. We empirically show that this solution is not
efficient in practice and propose an alternative approach. More precisely, we
show that, if the chase terminates for any equality axiomatisation of an
ontology, then it terminates for the original ontology (which may contain
equality). Therefore, one can apply existing acyclicity notions to check chase
termination over an axiomatisation of an ontology and then use the original
ontology for reasoning. We show that, in practice, doing so results in a more
efficient reasoning procedure. Furthermore, we present equality model-faithful
acyclicity, a general acyclicity notion that can be directly applied to
ontologies with equality
Extracting new knowledge from web tables: Novelty or confidence?
To extend the coverage of Knowledge Bases (KBs), it is useful to integrate factual information from public tabular data. Ideally, the extracted information should not only be correct, but also novel. So far, the evaluation of state-of-the-art techniques for this task has focused primarily on the correctness of the extractions, but the novelty is less well analysed. To fill this gap, we replicated the evaluation of two state-of-the-art techniques and analyse the amount of novel extractions using two new metrics. We observe that current techniques are biased towards confidence, but this comes at the expense of novelty. We sketch a possible solution for this problem as part of our ongoing research
Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts. We introduce a new technique for table interpretation based on a scalable graphical model using entity similarities. Our method further disambiguates cell values using KG embeddings as additional ranking method. Other distinctive features are the lack of assumptions about the underlying KG and the enabling of a fine-grained tuning of the precision/recall trade-off of extracted facts. Our experiments show that our approach has a higher recall during the interpretation process than the state-of-the-art, and is more resistant against the bias observed in extracting mostly redundant facts since it produces more novel extractions
Improvement of Urinary Stones Analysis Combining Morphological Analysis and Infrared Spectroscopy
Daudon et al. have developed a complex morphoconstitutional classification of renal stone in six different morphological types and several subtypes. According to this classification, a precise correspondence exists between causes of renal stones and subtypes with a great clinical relevance and can be considering a sort of shortcut for the metabolic diagnosis in renal stone patients. Now the diagnosis of causes of renal stones generally requires repeated biochemical investigations on urine and blood samples and usually remains presumptive. We analyzed 150 urinary stones both by stereoscopic microscopy and Fourier transform infrared spectroscopy. The comparison of 150 stones did not reveal any disagreement. We have only 20 partial agreement, and clinicians agreed that the imprecise information obtained with morphological analysis alone would have missed an important clinical finding only in 3 cases. In conclusion, in our opinion, the analysis of urinary stone must combine two different analytical techniques: morphological analysis by stereomicroscope and biochemical analysis with the FT-IR
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