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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.


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TL;DR: This article used a large collection of linguistic and visual cues, such as appearance, size, and position of entity bounding boxes, adjectives that contain attribute information, and spatial relationships between pairs of entities connected by verbs or prepositions.
Abstract: This paper presents a framework for localization or grounding of phrases in images using a large collection of linguistic and visual cues. We model the appearance, size, and position of entity bounding boxes, adjectives that contain attribute information, and spatial relationships between pairs of entities connected by verbs or prepositions. Special attention is given to relationships between people and clothing or body part mentions, as they are useful for distinguishing individuals. We automatically learn weights for combining these cues and at test time, perform joint inference over all phrases in a caption. The resulting system produces state of the art performance on phrase localization on the Flickr30k Entities dataset and visual relationship detection on the Stanford VRD dataset.

97 citations

Journal ArticleDOI
TL;DR: This article proposes to learn tag-specific composition functions and tag embeddings in recursive neural networks, and proposes to utilize POS tags to control the gates of tree-structured LSTM networks.
Abstract: Phrase/Sentence representation is one of the most important problems in natural language processing. Many neural network models such as Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Long Short-Term Memory (LSTM) have been proposed to learn representations of phrase/sentence, however, rich syntactic knowledge has not been fully explored when composing a longer text from its shorter constituent words. In most traditional models, only word embeddings are utilized to compose phrase/sentence representations, while the syntactic information of words is yet to be explored. In this article, we discover that encoding syntactic knowledge (part-of-speech tag) in neural networks can enhance sentence/phrase representation. Specifically, we propose to learn tag-specific composition functions and tag embeddings in recursive neural networks, and propose to utilize POS tags to control the gates of tree-structured LSTM networks. We evaluate these models on two benchmark datasets for sentiment classification, and demonstrate that improvements can be obtained with such syntactic knowledge encoded.

97 citations

Proceedings ArticleDOI
01 Dec 2013
TL;DR: This paper proposes motion atom and phrase as a mid-level temporal ``part'' for representing and classifying complex action, and introduces a bottom-up phrase construction algorithm and a greedy selection method for this mining task.
Abstract: This paper proposes motion atom and phrase as a mid-level temporal ``part'' for representing and classifying complex action. Motion atom is defined as an atomic part of action, and captures the motion information of action video in a short temporal scale. Motion phrase is a temporal composite of multiple motion atoms with an AND/OR structure, which further enhances the discriminative ability of motion atoms by incorporating temporal constraints in a longer scale. Specifically, given a set of weakly labeled action videos, we firstly design a discriminative clustering method to automatically discover a set of representative motion atoms. Then, based on these motion atoms, we mine effective motion phrases with high discriminative and representative power. We introduce a bottom-up phrase construction algorithm and a greedy selection method for this mining task. We examine the classification performance of the motion atom and phrase based representation on two complex action datasets: Olympic Sports and UCF50. Experimental results show that our method achieves superior performance over recent published methods on both datasets.

97 citations

Patent
12 Aug 1997
TL;DR: In this paper, a method and apparatus for mining text databases, employing sequential pattern phrase identification and shape queries, to discover trends is presented, where a maximum and minimum gap between words in the phrases and the minimum support all phrases must meet for the selected time period are specified.
Abstract: A method and apparatus for mining text databases, employing sequential pattern phrase identification and shape queries, to discover trends. The method passes over a desired database using a dynamically generated shape query. Documents within the database are selected based on specific classifications and user defined partitions. Once a partition is specified, transaction IDs are assigned to the words in the text documents depending on their placement within each document. The transaction IDs encode both the position of each word within the document as well as representing sentence, paragraph, and section breaks, and are represented in one embodiment as long integers with the sentence boundaries. A maximum and minimum gap between words in the phrases and the minimum support all phrases must meet for the selected time period may be specified. A generalized sequential pattern method is used to generate those phrases in each partition that meet the minimum support threshold. The shape query engine takes the set of phrases for the partition of interest and selects those that match a given shape query. A query may take the form of requesting a trend such as "recent upwards trend", "recent spikes in usage", "downward trends", and "resurgence of usage". Once the phrases matching the shape query are found, they are presented to the user.

97 citations

Journal ArticleDOI
TL;DR: It is argued that earlier claims that intonation is not necessary for correct turn-end projection are misguided, and that research on turn-taking should continue to considerintonation as a source of turn- end cues along with other linguistic and communicative phenomena.

97 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023467
20221,079
2021360
2020470
2019525
2018535