Phrase

About: Phrase is a research topic. Over the lifetime, 12580 publications have been published within this topic receiving 317823 citations. The topic is also known as: syntagma & phrases.

Right-Dislocation as scrambling

Abstract: The present paper shows that Right-Dislocation (RD) in Japanese shares a number of characteristics with scrambling, but nonetheless cannot be identified as rightward scrambling. The proposed solution to this apparent contradiction is that there is no direct syntactic movement of the right-dislocated phrase. Rather, the right-dislocated phrase is a remnant of an extra clause which is deleted (or sluiced) after scrambling. It is therefore concluded that RD involves leftward movement (scrambling) and that its rightward effect is only apparent. The proposed analysis is supported by a number of facts that have not previously been reported, including the distribution of adverbs, pronominal coreference, anaphor binding, idiom interpretations and wh-questions. The proposed analysis is also consistent with Kayne’s (1994) proposal that there are no rightward movement processes in syntax.

Knowledge Aided Consistency for Weakly Supervised Phrase Grounding

Abstract: Given a natural language query, a phrase grounding system aims to localize mentioned objects in an image. In weakly supevised scenario, mapping between image regions (i.e., proposals) and language is not available in the training set. Previous methods address this deficiency by training a grounding system via learning to reconstruct language information contained in input queries from predicted proposals. However, the optimization is solely guided by the reconstruction loss from the language modality, and ignores rich visual information contained in proposals and useful cues from external knowledge. In this paper, we explore the consistency contained in both visual and language modalities, and leverage complementary external knowledge to facilitate weakly supervised grounding. We propose a novel Knowledge Aided Consistency Network (KAC Net) which is optimized by reconstructing input query and proposal's information. To leverage complementary knowledge contained in the visual features, we introduce a Knowledge Based Pooling (KBP) gate to focus on query-related proposals. Experiments show that KAC Net provides a significant improvement on two popular datasets.

Safety symbol comprehension: effects of symbol type, familiarity and age

Abstract: A new procedure for evaluating symbol comprehension, the phrase generation procedure, was assessed with 52 younger and 52 older adults. Participants generated as many phrases as came to mind when viewing 40 different safety symbols (hazard alerting, mandatory action, prohibition, and information symbols). Symbol familiarity was also assessed. Comprehension rates for both groups were lower than the 85% level recommended by the American National Standards Institute. Moreover, older participants' comprehension was significantly worse than younger participants', and the older adults also generated significantly fewer phrases. Generally, prohibition symbols were comprehended best and hazard alerting symbols worst. In addition, symbol familiarity was positively correlated with symbol comprehension. These findings indicate that important safety information depicted on signs and household products may be misunderstood if presented in symbolic form. Furthermore, certain types of symbols may be better understood (e.g., prohibition symbols) than other types (e.g., hazard alerting symbols) by both younger and older individuals. These findings signify the utility of the phrase generation procedure as a method for evaluating symbol comprehension, particularly when it is not possible or desirable to provide contextual information. Actual or potential applications of this research include using the phrase generation approach to identify poorly comprehended symbols, including identification of critical confusions that may arise when processing symbolic information.

Who Am I? Personality Detection Based on Deep Learning for Texts

Abstract: Recently, personality detection based on texts from online social networks has attracted more and more attentions. However, most related models are based on letter, word or phrase, which is not sufficient to get good results. In this paper, we present our preliminary but interesting and useful research results to show that the structure of texts can be also an important feature in the study of personality detection from texts. We propose a model named 2CLSTM, which is a bidirectional LSTMs (Long Short Term Memory networks) concatenated with CNN (Convolutional Neural Network), to detect user's personality using structures of texts. Besides, a concept, Latent Sentence Group (LSG), is put forward to express the abstract feature combination based on closely connected sentences and we use our model to capture it. To the best of our knowledge, most related works only conducted their experiments on one data set, which may not well explain the versatility of their models. We implement our evaluations on two different kinds of datasets, containing long texts and short texts. Evaluations on both datasets have achieved better results, which demonstrate that our model can efficiently learn valid text structure features to accomplish the task.

Proverb and idiom comprehension in Alzheimer disease.

Abstract: Twenty-nine patients diagnosed with Probable Alzheimer Disease were administered tests of word, familiar phrases (idioms and proverbs), and novel phrase comprehension. From the early stage of the disease, patients performed worse at understanding familiar phrases than single words or novel phrases. The results uphold common observations that AD patients have difficulty interpreting abstract meanings. Cognitive variables responsible for poor idiom/proverb comprehension and the clinical implications of this new protocol are discussed.