Question: What are the fields included in A TA application?
Evidence: Figure FIGREF23 depicts overall process flow for enabling automated feature recommendations.
For a new text analytics application requiring feature engineering, it starts with estimating its semantic proximity (from the perspective of a NLP data scientist) with existing applications with known features. Based upon these proximity estimates as well as expected relevance of features for existing applications, system would recommend features for the new application in a ranked order. Furthermore, if user's selections are not aligned with system's recommendations, system gradually adapts its recommendation so that eventually it can achieve alignment with user preferences.
Towards that let us start with characterizing text analytics applications. A TA application's details should include following fields:
Text based description of an TA application (or problem). For example, “identify medical procedures being referenced in a discharge summary” or “what are the input and output entities mentioned in a software requirements specification”.
Analysis unit at which features are to be specified and training annotations are available, and ML model is designed to give outcomes. Options include word, phrase, sentence, paragraph, or document.
Specifies technical classification of the underlying ML challenge with respect to a well-defined ontology. E.g., Classification (with details), Clustering, etc.
Specifies how performance of the ML model is to be measured - again should be specified as per some well defined ontology.
Knowledge base of text analytics applications contains details for text analytics applications in above specified format. Each application is further assumed to be associated with a set of Features (or feature types) specified in nlpFSpL together with their relevance scores against a performance metric. Relevance score of a feature is a measure of the extent to which this feature contributes to achieve overall performance of ML model while solving the underlying application. Relevance score may be estimated using any of the known feature selection metrics BIBREF25.
To specify knowledge base formally, let us assume that there are $m$ different applications and $k$ unique feature specifications across these applications applying same performance metric. Let us denote these as follows: $APPS=\lbrace App_1,\ldots , App_m\rbrace $ and ${\mathit {\Theta }}_F$ = $\left\lbrace F_1,F_2,\dots ,F_k\right\rbrace $ respectively. Knowledge base is then represented as a feature co-occurrence matrix $PF_{m\times k}$ such that $PF[i,j] = \delta _{i,F_j}$ is the relevance score of $j^{th}$ feature specification ($F_j\in \mathit {\Theta }_F$) for $i^{th}$ application $App_i\in APPS$.
Towards that let us start with characterizing text analytics applications. A TA application’s details should include following fields: Problem Description Text based description of an TA application (or problem). For example, “identify medical procedures being referenced in a discharge summary” or “what are the input and output entities mentioned in a software requirements specificationâA˘ ˙I. Annotation Level Analysis unit at which features are to be specified and training annotations are available, and ML model is designed to give outcomes. Options include word, phrase, sentence, paragraph, or document. Problem Type Specifies technical classification of the underlying ML challenge with respect to a well-defined ontology. E.g., Classification (with details), Clustering, etc. Performance Metric Specifies how performance of the ML model is to be measured - again should be specified as per some well defined ontology.
Answer: They are Problem Description, Annotation Level, Problem Type, and Performance Metric.