Tests of Sample-recovery Models of Cued Recall

Sample-recovery models are a predominant class of episodic memory models that seek to explain why sometimes the representation of an experienced event is not retrieved or retrieved incorrectly. In these models, a correct retrieval occurs if the correct target item was sampled among the alternative studied item, then recovered correctly. In cued recall, participants output the representation of a single experienced event, a target, given a presented test stimulus and some defined relationship between the stimulus and the target. This relationship depends on the kind of cued recall and can rely on either studied or pre-experimental relationships. Sample-recovery models of this task share common testable properties related to both sampling and recovery, which we do across two experiments. Experiment 1 tests the property that sampling in sample-recovery models of cued recall is one process: they combine information about test stimulus and its relationship to the target into a single value and sample in a way consistent with the Luce choice rule. We test this assumption by testing whether manipulating the strengths of these relationships generates differential influence on performance in kinds of cued recall where different relationships between test stimulus and response are probed. The pattern of data is inconsistent with one sample process but is consistent with a sampling procedure that separately samples for a cue given the stimulus and a target given a cue. Experiment 2 tests the assumption that recovery performance is independent of other studied items. We allow some cue and target words to be related to some other untested studied words. Targets with a related word on the study list were associated with more correct responses than targets without one. This suggests that recovery in some way uses the memory for the other studied items to help retrieve. We consider how various models of sample-recovery may be adapted to account for these findings, with a particular focus on the Retrieving Effectively from Memory model

Tests Of Sample-recovery Models Of Cued Recall

Sample-recovery models are a predominant class of episodic memory models that seek to explain why sometimes the representation of an experienced event is not retrieved or retrieved incorrectly. In these models, a correct retrieval occurs if the correct target item was sampled among the alternative studied item, then recovered correctly. In cued recall, participants output the representation of a single experienced event, a target, given a presented test stimulus and some defined relationship between the stimulus and the target. This relationship depends on the kind of cued recall and can rely on either studied or pre-experimental relationships. Sample-recovery models of this task share common testable properties related to both sampling and recovery, which we do across two experiments. Experiment 1 tests the property that sampling in sample-recovery models of cued recall is one process: they combine information about test stimulus and its relationship to the target into a single value and sample in a way consistent with the Luce choice rule. We test this assumption by testing whether manipulating the strengths of these relationships generates differential influence on performance in kinds of cued recall where different relationships between test stimulus and response are probed. The pattern of data is inconsistent with one sample process but is consistent with a sampling procedure that separately samples for a cue given the stimulus and a target given a cue. Experiment 2 tests the assumption that recovery performance is independent of other studied items. We allow some cue and target words to be related to some other untested studied words. Targets with a related word on the study list were associated with more correct responses than targets without one. This suggests that recovery in some way uses the memory for the other studied items to help retrieve. We consider how various models of sample-recovery may be adapted to account for these findings, with a particular focus on the Retrieving Effectively from Memory model

A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

In this paper, we propose a hybrid approach for sentence paraphrase identification. The proposal addresses the problem of evaluating sentence-to-sentence semantic similarity when the sentences contain a set of named-entities. The essence of the proposal is to distinguish the computation of the semantic similarity of named-entity tokens from the rest of the sentence text. More specifically, this is based on the integration of word semantic similarity derived from WordNet taxonomic relations, and named-entity semantic relatedness inferred from Wikipedia entity co-occurrences and underpinned by Normalized Google Distance. In addition, the WordNet similarity measure is enriched with word part-of-speech (PoS) conversion aided with a Categorial Variation database (CatVar), which enhances the lexico-semantics of words. We validated our hybrid approach using two different datasets; Microsoft Research Paraphrase Corpus (MSRPC) and TREC-9 Question Variants. In our empirical evaluation, we showed that our system outperforms baselines and most of the related state-of-the-art systems for paraphrase detection. We also conducted a misidentification analysis to disclose the primary sources of our system errors

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field