Распределенные представления слов
Related Papers
A Probabilistic Model for Learning Multi-Prototype Word Embeddings
Distributed word representations have been widely used and proven to be useful in quite a few natural language processing and text mining tasks. Most of existing word embedding models aim …...
Using distributed word representations for robust semantic role labeling (Utilisation de représentations de mots pour l’étiquetage de rôles sémantiques suivant FrameNet) [in French]
. According to Frame Semantics (Fillmore 1976), words’ meaning are best understood considering the semantic frame they play a role in, for the frame is what gives them context. FrameNet …...
Component-Enhanced Chinese Character Embeddings
Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on En- glish. In this work, we innovatively …...
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation
This paper reports our submissions to seman- tic textual similarity task, i.e., task 2 in Se- mantic Evaluation 2015. We built our sys- tems using various traditional features, such as …...
Empty Category Detection With Joint Context-Label Embeddings
This paper presents a novel technique for empty category (EC) detection using dis- tributed word representations. A joint model is learned from the labeled data to map both the distributed …...
MITRE: Seven Systems for Semantic Similarity in Tweets
This paper describes MITRE’s participation in the Paraphrase and Semantic Similar- ity in Twitter task (SemEval-2015 Task 1). This effort placed first in Semantic Similar- ity and second in Paraphrase …...
Exploring Semantic Representation in Brain Activity Using Word Embeddings
In this paper, we utilize distributed word rep- resentations (i.e., word embeddings) to anal- yse the representation of semantics in brain activity. The brain activity data were recorded using functional …...
Out-of-domain FrameNet Semantic Role Labeling
AbstractDomain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, …...
Contextualized Word Representations for Multi-Sense Embedding
Distributed word representations are used in many natural language processing tasks. When dealing with ambiguous words, it is de- sired to generate multi-sense embeddings, i.e., multiple representations per word. Therefore, …...
IsoScore: Measuring the Uniformity of Embedding Space Utilization
AbstractThe recent success of distributed word representations has led to an increased interest in analyzing the properties of their spatial distribution. Several studies have suggested that contextualized word embedding models …...