Experiment 3 Sample Clauses
Experiment 3 general agreement.
1. Firs of all, the data to be compared in an “all words” corpus are not homo- geneous. When kappa measure is applied to inter-annotation agreement in word sense annotation, the possible senses of a word are the classes in which the word could be classified. These classes must be homogeneous. In an “all words” corpus they are not homogeneous because there are words with one sense, words with two, three senses, etc. Only the annotation of word with the same amount of senses could be compared with kappa measure.
2. However, the senses of two ambiguous words with the same number of sen- ses are not comparable: the senses of some words are very different (so it is easy to select between them the correct one) and the senses of other words are very similar (so it is very difficult to annotate these words). This diffe- rences in the senses are not reflected in kappa statistics. For kappa, all senses (all classes) are equal. [3] proposes a solution to these problems. They propose calculate two ▇▇▇▇▇- ges: micro-kappa average and macro-kappa average. The first one is based on the average of all words (micro-kappa) and the second one is based on the average of the kappa measure of each word (macro-kappa). In order to evaluate the semantic annotation of Cast3LB, in this paper we pro- pose two measures of agreement: minimum level of agreement and general agree- ment. Minimum level of agreement is calculated based only on words with high level of ambiguity. For this measure kappa is used. General agreement is calculated for all words annotated in a sample of the corpus (nouns, verbs and adjectives).
Experiment 3. In experiment 3, we tested whether manipulating a priori response bias impacts the effect of attractor number in grammatical sentences.
6.1.3.1 Cue-based retrieval account Under the assumptions of the cue-based retrieval account, changing a priori bias would not result in an amplified effect of attractor number in grammatical sentences. This lack of increase is because attraction is expected to surface only in cases with more than a single match. In grammatical sentences, the dependency between the verb and the subject is satisfied without any problem. The number and the subjecthood features, such as case and syntactic position, fully match the cues provided by the verb in grammatical sentences. Thus, no other candidate for the agreement controller role should be entertained. This understanding of attraction effects necessitates that the grammaticality asymmetry is due to the characteristics of sentence processing. Given the assumptions of this model, we believe that the cue-based retrieval account would expect no attraction effects when the a priori response bias is manipulated.
6.1.3.2 ▇▇▇▇▇▇▇ and morphing account A priori response bias of the participants is not implemented directly in the Marking and Morphing account. As far as we know, this account is not equipped to integrate response bias into the attraction phenomenon. However, unlike the cue-based retrieval account, The Marking and Morphing account does not expect a grammaticality asymmetry: the effect of plural attractor should be comparable in grammatical and ungrammatical sentences. While this asymmetry is a direct result of the sentence processing mechanisms in the cue-based retrieval phenomenon and is intertwined with the attraction process, the Marking and Morphing account is agnostic to this phenomenon. A recent study by ▇▇▇▇▇▇▇▇ et al. (2019) showed that this asymmetry is related to the nature of linguistic experimenting. They argue that participants have a general tendency to answer yes more often than no. They utilized ▇▇▇▇▇▇▇▇’▇ (1978) Drift Diffusion Model and showed that when the extra-linguistic factor bias is controlled, the predictions of the Marking and Morphing account hold. We reasoned that ▇▇▇▇▇▇▇▇ et al.’s (2019) data, manipulation, and findings should be replicated, given that the Drift Diffusion model account of decision making is not limited to a particular language, a particular structure, or a particular demographic. That is, participants with no a priori bias towards yes...
Experiment 3. The joint impact of social network local-clustering and individual incentives on population smoking behavior pattern.