scholarly journals SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation

2015 ◽  
Vol 41 (4) ◽  
pp. 665-695 ◽  
Author(s):  
Felix Hill ◽  
Roi Reichart ◽  
Anna Korhonen
Keyword(s):  
Gold Standard ◽  
Free Association ◽  
Representation Learning ◽  
Fine Grained ◽  
Similarity Estimation ◽  
Semantic Models ◽  
Distributional Semantic Models ◽  
Learning Architectures ◽  
Insight Into ◽  
Independent Rating

We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways. First, in contrast to gold standards such as WordSim-353 and MEN, it explicitly quantifies similarity rather than association or relatedness so that pairs of entities that are associated but not actually similar (Freud, psychology) have a low rating. We show that, via this focus on similarity, SimLex-999 incentivizes the development of models with a different, and arguably wider, range of applications than those which reflect conceptual association. Second, SimLex-999 contains a range of concrete and abstract adjective, noun, and verb pairs, together with an independent rating of concreteness and (free) association strength for each pair. This diversity enables fine-grained analyses of the performance of models on concepts of different types, and consequently greater insight into how architectures can be improved. Further, unlike existing gold standard evaluations, for which automatic approaches have reached or surpassed the inter-annotator agreement ceiling, state-of-the-art models perform well below this ceiling on SimLex-999. There is therefore plenty of scope for SimLex-999 to quantify future improvements to distributional semantic models, guiding the development of the next generation of representation-learning architectures.

Detecting linguistic idiosyncratic interests in autism using distributional semantic models

2014 ◽  
Author(s):  
Masoud Rouhizadeh ◽  
Emily Prud'hommeaux ◽  
Jan van Santen ◽  
Richard Sproat
Keyword(s):  
Semantic Models ◽  
Distributional Semantic Models

scholarly journals Catch me if you can: A participant-level rumor detection framework via fine-grained user representation learning

2021 ◽  
Vol 58 (5) ◽  
pp. 102678
Author(s):  
Xueqin Chen ◽  
Fan Zhou ◽  
Fengli Zhang ◽  
Marcello Bonsangue
Keyword(s):  
Representation Learning ◽  
Fine Grained ◽  
Rumor Detection

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

Exploring vowel formant estimation through simulation-based techniques

2020 ◽  
Vol 6 (s1) ◽  
Author(s):  
Tyler Kendall ◽  
Charlotte Vaughn
Keyword(s):  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Fine Grained ◽  
Vowel Formant ◽  
Simulation Based ◽  
Insight Into

AbstractThis paper contributes insight into the sources of variability in vowel formant estimation, a major analytic activity in sociophonetics, by reviewing the outcomes of two simulations that manipulated the settings used for linear predictive coding (LPC)-based vowel formant estimation. Simulation 1 explores the range of frequency differences obtained when minor adjustments are made to LPC settings, and measurement timepoints around the settings used by trained analysts, in order to determine the range of variability that should be expected in sociophonetic vowel studies. Simulation 2 examines the variability that emerges when LPC settings are varied combinatorially around constant default settings, rather than settings set by trained analysts. The impacts of different LPC settings are discussed as a way of demonstrating the inherent properties of LPC-based formant estimation. This work suggests that differences more fine-grained than about 10 Hz in F1 and 15–20 Hz in F2 are within the range of LPC-based formant estimation variability.

Fine-Grained Privacy Detection with Graph-Regularized Hierarchical Attentive Representation Learning

2020 ◽  
Vol 38 (4) ◽  
pp. 1-26
Author(s):  
Xiaolin Chen ◽  
Xuemeng Song ◽  
Ruiyang Ren ◽  
Lei Zhu ◽  
Zhiyong Cheng ◽  
...  
Keyword(s):  
Representation Learning ◽  
Fine Grained

scholarly journals Unsupervised Compositionality Prediction of Nominal Compounds

2019 ◽  
Vol 45 (1) ◽  
pp. 1-57 ◽  
Author(s):  
Silvio Cordeiro ◽  
Aline Villavicencio ◽  
Marco Idiart ◽  
Carlos Ramisch
Keyword(s):  
Morphological Variation ◽  
Red Wine ◽  
Data Sets ◽  
High Agreement ◽  
Semantic Models ◽  
Corpus Size ◽  
Nominal Compounds ◽  
Distributional Semantic Models ◽  
The Impact

Nominal compounds such as red wine and nut case display a continuum of compositionality, with varying contributions from the components of the compound to its semantics. This article proposes a framework for compound compositionality prediction using distributional semantic models, evaluating to what extent they capture idiomaticity compared to human judgments. For evaluation, we introduce data sets containing human judgments in three languages: English, French, and Portuguese. The results obtained reveal a high agreement between the models and human predictions, suggesting that they are able to incorporate information about idiomaticity. We also present an in-depth evaluation of various factors that can affect prediction, such as model and corpus parameters and compositionality operations. General crosslingual analyses reveal the impact of morphological variation and corpus size in the ability of the model to predict compositionality, and of a uniform combination of the components for best results.

Bone Grafts: An Overview of Bone Remodeling, Types and Recent Advances

2021 ◽  
pp. 6101-6105
Author(s):  
Gopala Krishna Ganta ◽  
Rama Krishna Alla ◽  
Kamala Cheruvu ◽  
Bharathi Ram Guduri
Keyword(s):  
Bone Graft ◽  
Bone Remodeling ◽  
Gold Standard ◽  
Bone Grafts ◽  
Form And Function ◽  
Recent Advances ◽  
And Function ◽  
Insight Into

Bone grafts are often used to retrieve the lost bone in the most acceptable, technical and skilful manner that enables to restore the form and function of the bone. Numerous bone graft materials have been developed to fill and/or remodel the bony defects. Though, autografts were considered to be the gold standard among the grafts available; they have got some inherent disadvantages. The current research is more focused on allografts, which addressed the problems associated with autografts. This article provides an insight into the remodeling process, and various types of bone grafts currently available. Also, the emphasis was given on the recent advances of the bone grafts.

scholarly journals Extraction of Terms Related to Named Rivers

Languages ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 46
Author(s):  
Juan ◽  
Faber
Keyword(s):  
Knowledge Base ◽  
Mississippi River ◽  
Environmental Science ◽  
Automatic Method ◽  
Statistical Procedures ◽  
Conceptual Structures ◽  
Semantic Models ◽  
Semantic Frames ◽  
Conceptual Network ◽  
Distributional Semantic Models

EcoLexicon is a terminological knowledge base on environmental science, whose design permits the geographic contextualization of data. For the geographic contextualization of landform concepts, this paper presents a semi-automatic method for extracting terms associated with named rivers (e.g., Mississippi River). Terms were extracted from a specialized corpus, where named rivers were automatically identified. Statistical procedures were applied for selecting both terms and rivers in distributional semantic models to construct the conceptual structures underlying the usage of named rivers. The rivers sharing associated terms were also clustered and represented in the same conceptual network. The results showed that the method successfully described the semantic frames of named rivers with explanatory adequacy, according to the premises of Frame-Based Terminology.

scholarly journals Using free association networks to extract characteristic patterns of affect dynamics

2020 ◽  
Vol 476 (2236) ◽  
pp. 20190647
Author(s):  
Yaniv Dover ◽  
Zohar Moore
Keyword(s):  
Equilibrium Point ◽  
Free Association ◽  
Metastable Equilibrium ◽  
Association Network ◽  
Global Equilibrium ◽  
Valence And Arousal ◽  
Rich Data ◽  
Insight Into ◽  
Over Time ◽  
Affect Dynamics

The dynamics of human affect in day-to-day life are an intrinsic part of human behaviour. Yet, it is difficult to observe and objectively measure how affect evolves over time with sufficient resolution. Here, we suggest an approach that combines free association networks with affect mapping, to gain insight into basic patterns of affect dynamics. This approach exploits the established connection in the literature between association networks and behaviour. Using extant rich data, we find consistent patterns of the dynamics of the valence and arousal dimensions of affect. First, we find that the individuals represented by the data tend to feel a constant pull towards an affect-neutral global equilibrium point in the valence–arousal space. The farther the affect is from that point, the stronger the pull. We find that the drift of affect exhibits high inertia, i.e. is slow-changing, but with occasional discontinuous jumps of valence. We further find that, under certain conditions, another metastable equilibrium point emerges on the network, but one which represents a much more negative and agitated state of affect. Finally, we demonstrate how the affect-coded association network can be used to identify useful or harmful trajectories of associative thoughts that otherwise are hard to extract.

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