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"Orthogonal" is often used to mean "independent", as in "independent variable which does not correlate with the other variables". I believe this terminology originates from principal component analysis, where uncorrelated variation would be along orthogonal axes. Or, in the words of the Wikipedia article on orthogonality applied ...


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What you want to look for is called anaphora resolution. You basically keep a record of the past conversation and try and find an antecedent for any occurrences of it, he/she, her/his, etc. You probably want to have a pre-processing step where you substitute the antecedent before passing the input sentence on to the agent.


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Looking at the paper, it seems to me that they are not using orthogonal in a literal, mathematics (or geometric) sense. Instead, I read that as two things (especially since the word "ablation" appears later in the sentence): They are attempting to use lots of fancy words They are simply indicating that these changes are separate from and have no ...


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Have a look at Named Entity Recognition (NER); these algorithms are mainly concerned with recognising that there is an entity, but often also include normalising the name to a canonical form for information retrieval -- this is what you would need. In a previous job I actually implemented this, using a fuzzy match with variable word order. You would still ...


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Precision and Recall are concepts that have been introduced in the field of information retrieval. Imagine you have a large set of documents, and you want to find the ones that are relevant to a particular issue. You can be sure to find all relevant documents if you simply return the whole lot -- you won't miss a single relevant document. So your recall is 1....


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It means precisely the same as true/false positive and true/false negative in the classic formulation of precision, recall, F-score for classification tasks. relevant and retrieved: true positive relevant and not retrieved: false positive not relevant and retrieved: false negative not relevant and not retrieved: true negative And yes, the relevance depends ...


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I think that literature is simply inconsistent in this regard. But here's a distinction that I think helps to shade a bit of light on this question: text representation: as you said we have to convert text into numerical variables. This term refers to general strategies to convert text into numbers, like embedding, bag of words, and so on. text features ...


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The problem: You are facing a Natural Language problem called Named Entity Recognition (that's the key word you are looking for). But before you dive deep into it, have in mind it's best suited for user input data (where users are absolutely chaotic) and it looks like you have a system data. The right way: You should have some kind of tabular (structured) ...


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You could handwrite different templates and choose probabilistically, according to writing style or pragmatic effects like irony and so on, but that very much depends on the domain. If you have tabular data, from which you want to generate text, you should probably forget about GPT and so on. You only have few control (despite copy mechanism) over the ...


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