scholarly journals Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases

2018 ◽  
Vol 18 (S2) ◽  
Author(s):  
Zhiwei Chen ◽  
Zhe He ◽  
Xiuwen Liu ◽  
Jiang Bian
Keyword(s):  
Domain Knowledge ◽  
Knowledge Bases ◽  
Semantic Relations ◽  
Word Embeddings ◽  
General Domain

scholarly journals Lexical Sememe Prediction via Word Embeddings and Matrix Factorization

2017 ◽  
Author(s):  
Ruobing Xie ◽  
Xingchi Yuan ◽  
Zhiyuan Liu ◽  
Maosong Sun
Keyword(s):  
Knowledge Base ◽  
Real World ◽  
Matrix Factorization ◽  
Knowledge Bases ◽  
Semantic Relations ◽  
Linguistic Knowledge ◽  
Word Embeddings ◽  
New Words ◽  
First Time

Sememes are defined as the minimum semantic units of human languages. People have manually annotated lexical sememes for words and form linguistic knowledge bases. However, manual construction is time-consuming and labor-intensive, with significant annotation inconsistency and noise. In this paper, we for the first time explore to automatically predict lexical sememes based on semantic meanings of words encoded by word embeddings. Moreover, we apply matrix factorization to learn semantic relations between sememes and words. In experiments, we take a real-world sememe knowledge base HowNet for training and evaluation, and the results reveal the effectiveness of our method for lexical sememe prediction. Our method will be of great use for annotation verification of existing noisy sememe knowledge bases and annotation suggestion of new words and phrases.

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

scholarly journals Learning Lexical Subspaces in a Distributional Vector Space

2020 ◽  
Vol 8 ◽  
pp. 311-329
Author(s):  
Kushal Arora ◽  
Aishik Chakraborty ◽  
Jackie C. K. Cheung
Keyword(s):  
Vector Space ◽  
Semantic Relations ◽  
Distributional Semantics ◽  
Word Embeddings ◽  
Word Similarity ◽  
Lexical Semantic ◽  
Novel Approach ◽  
Classification Tasks

In this paper, we propose LexSub, a novel approach towards unifying lexical and distributional semantics. We inject knowledge about lexical-semantic relations into distributional word embeddings by defining subspaces of the distributional vector space in which a lexical relation should hold. Our framework can handle symmetric attract and repel relations (e.g., synonymy and antonymy, respectively), as well as asymmetric relations (e.g., hypernymy and meronomy). In a suite of intrinsic benchmarks, we show that our model outperforms previous approaches on relatedness tasks and on hypernymy classification and detection, while being competitive on word similarity tasks. It also outperforms previous systems on extrinsic classification tasks that benefit from exploiting lexical relational cues. We perform a series of analyses to understand the behaviors of our model. 1 Code available at https://github.com/aishikchakraborty/LexSub .

scholarly journals Word embeddings for application in geosciences: development, evaluation and examples of soil-related concepts

2019 ◽  
Author(s):  
José Padarian ◽  
Ignacio Fuentes
Keyword(s):  
Language Processing ◽  
Dimensional Space ◽  
Language Model ◽  
Test Suite ◽  
Word Embeddings ◽  
General Domain ◽  
Domain Specific ◽  
Descriptive Information ◽  
Development Evaluation ◽  
Numerical Representations

Abstract. A large amount of descriptive information is available in most disciplines of geosciences. This information is usually considered subjective and ill-favoured compared with its numerical counterpart. Considering the advances in natural language processing and machine learning, it is possible to utilise descriptive information and encode it as dense vectors. These word embeddings lay on a multi-dimensional space where angles and distances have a linguistic interpretation. We used 280 764 full-text scientific articles related to geosciences to train a domain-specific language model capable of generating such embeddings. To evaluate the quality of the numerical representations, we performed three intrinsic evaluations, namely: the capacity to generate analogies, term relatedness compared with the opinion of a human subject, and categorisation of different groups of words. Since this is the first attempt to evaluate word embedding for tasks in the geosciences domain, we created a test suite specific for geosciences. We compared our results with general domain embeddings commonly used in other disciplines. As expected, our domain-specific embeddings (GeoVec) outperformed general domain embeddings in all tasks, with an overall performance improvement of 107.9 %. The resulting embedding and test suite will be made available for other researchers to use an expand.

scholarly journals The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings

2019 ◽  
Vol 84 (5) ◽  
pp. 905-949 ◽  
Author(s):  
Austin C. Kozlowski ◽  
Matt Taddy ◽  
James A. Evans
Keyword(s):  
Dimensional Space ◽  
Historical Analysis ◽  
Cultural Dimensions ◽  
Semantic Relations ◽  
Relational Model ◽  
High Dimensional ◽  
Word Embeddings ◽  
Culture Dimensions ◽  
Economic Transformations ◽  
Shared Understandings

We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture. Dimensions induced by word differences ( rich – poor) in these spaces correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared associations, which we validate with surveys. Analyzing text from millions of books published over 100 years, we show that the markers of class continuously shifted amidst the economic transformations of the twentieth century, yet the basic cultural dimensions of class remained remarkably stable. The notable exception is education, which became tightly linked to affluence independent of its association with cultivated taste.

It's Time To Examine the Nomological Net of Job Knowledge

2016 ◽  
Vol 9 (1) ◽  
pp. 51-55 ◽  
Author(s):  
W. Jackeline Torres ◽  
Margaret E. Beier
Keyword(s):  
Negative Feedback ◽  
Positive Feedback ◽  
Domain Knowledge ◽  
Work Life ◽  
Situational Judgment ◽  
Situational Judgment Tests ◽  
General Domain ◽  
Job Knowledge

Lievens and Motowidlo (2016) argue compellingly that situational judgment tests (SJTs) measure job-relevant general domain knowledge, conceptualized as implicit trait policies (ITPs). ITPs are defined as a person's knowledge about the utility of expressing certain traits. They develop through the feedback a person receives when acting in accordance with their trait profiles in different environments (work, life, leisure). Positive feedback reinforces the knowledge that behavior in accordance with one's own traits is appropriate, and negative feedback reinforces the knowledge that an approach that differs from one's trait tendencies may be more effective. As such, ITPs represent a person's knowledge about the effectiveness of behaviors across a variety of contexts.

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