Most existing methods treat these problems as linear regression , learning to relate word frequencies and other simple features to a known response variable (e. g., voting intention polls or financial indicators).

The first makes a constant prediction of the mean value of the response variable y in the training set (By); the second predicts the last value of y (Blast); and the third baseline (LEN) is a linear regression over the terms using elastic net regularisation.

The main theme of the aforementioned works is linear regression between word frequencies and a real-world quantity.

5 and 7), where we individually learn {W, ,8} and then {U , fl }; each step of the process is a standard linear regression problem with an 61/62 regulariser.

Table 2: Heaps’ Linear Regression

As the curves in Figure 4 and the linear regression coefficients in Table 2 show, the growth rate of distinct words in both Sorani and Kurmanji Kurdish are higher than Persian and English.

Table 3: Zipf’s Linear Regression

Figure 3 shows a Precision-Recall (PR) curve for MATCHER and three baselines: a “Frequency” model that ranks candidate matches for TD by their frequency during the candidate identification step; a “Pattern” model that uses MATCHER’s linear regression model for ranking, but is restricted to only the pattern-based features; and an “Extractions” model that similarly restricts the ranking model to ReVerb features.

For W, we use a linear regression model whose features are the score from MATCHER, the probabilities from the Syn and Sem NBC models, and the average weight of all lexical entries in UBL with matching syntax and semantics.

The regression model is a linear regression with least-squares parameter estimation; we experimented with support vector regression models with nonlinear kernels, with no significant improvements in accuracy.