Neural networks are the state-of-the-art method of machine learning for many problems in NLP. Their success in machine translation and other NLP tasks is phenomenal, but their interpretability is challenging. We want to find out how neural networks r
epresent meaning. In order to do this, we propose to examine the distribution of meaning in the vector space representation of words in neural networks trained for NLP tasks. Furthermore, we propose to consider various theories of meaning in the philosophy of language and to find a methodology that would enable us to connect these areas.
Discontinuous entities pose a challenge to named entity recognition (NER). These phenomena occur commonly in the biomedical domain. As a solution, expansions of the BIO representation scheme that can handle these entity types are commonly used (i.e.
BIOHD). However, the extra tag types make the NER task more difficult to learn. In this paper we propose an alternative; a fuzzy continuous BIO scheme (FuzzyBIO). We focus on the task of Adverse Drug Response extraction and normalization to compare FuzzyBIO to BIOHD. We find that FuzzyBIO improves recall of NER for two of three data sets and results in a higher percentage of correctly identified disjoint and composite entities for all data sets. Using FuzzyBIO also improves end-to-end performance for continuous and composite entities in two of three data sets. Since FuzzyBIO improves performance for some data sets and the conversion from BIOHD to FuzzyBIO is straightforward, we recommend investigating which is more effective for any data set containing discontinuous entities.
Abstract ⚠ This paper contains prompts and model outputs that are offensive in nature. When trained on large, unfiltered crawls from the Internet, language models pick up and reproduce all kinds of undesirable biases that can be found in the data: Th
ey often generate racist, sexist, violent, or otherwise toxic language. As large models require millions of training examples to achieve good performance, it is difficult to completely prevent them from being exposed to such content. In this paper, we first demonstrate a surprising finding: Pretrained language models recognize, to a considerable degree, their undesirable biases and the toxicity of the content they produce. We refer to this capability as self-diagnosis. Based on this finding, we then propose a decoding algorithm that, given only a textual description of the undesired behavior, reduces the probability of a language model producing problematic text. We refer to this approach as self-debiasing. Self-debiasing does not rely on manually curated word lists, nor does it require any training data or changes to the model's parameters. While we by no means eliminate the issue of language models generating biased text, we believe our approach to be an important step in this direction.1
In this paper, detail technical and economic feasibility study are
implemented to use the improved solvent instead of the used
solvent in Syrian field (Sodium hydroxide, NaOH) until this time, to
prevent and remove the sulfur deposits in the gas w
ells that most
suffer from this problem.
Has also been confirmed on the technical best method for
suggested solvent injection and depend on the field data of the
studied wells /Jbissah-223, Jbissah-220/ to carrying out the required
economical calculations, and the sensitivity analyzing for changes
of economical with changes of dollar exchange rate and gas
production rate from studied wells are performed, and then the
curves of economic Feasibility study resulting from the
comparison process was drawing. And recommending the use of the
improved solvent where this solvent is the most economic solvent.
In this paper, a proposal a chemical solvent to remove sulfur deposits within the tubes production of the wells that most suffer from this problem are done. Where the study was conducted on the referenced study about the characteristics and forms of
sulfur in the nature, and explain the Scheme phasic of sulfur and required conditions for the deposition of elemental sulfur within the tubes production. And has been clarified the mechanism of deposition and the outline of a simplified process for the nuclei of molecules from supersaturation sulfur vapor. Has also been depend on the field data of the studied wells /Jbissah-223, Jbissah-220/ (taken from the daily operational reports and Historical Biography of the wells), to conducting laboratory experiments in order to compare the between used solvent at field (NaOH) and proposed solvent (tallow amine activated diethyl disulfides) in this study in terms of the melt the deposited sulfur within the tubes production. and as a result, the curves resulting from the comparison process was drawn and recommending the use of the proposed solvent in treatment the acid gas wells in Jbissah fields that suffer from this problem where this solvent is the most effective and economical.
