Semantic Knowledge
Related Papers
The Role of Semantic Processing in an Automatic Speech Understanding System
We present the semantics component of a speech understanding and dialogue system that is developed at our institute. Due to pronunciation variabilities and vagueness of the word recognition process, semantics …...
Anaphora Resolution: A Multi-Strategy Approach
Anaphora resolution has proven to be a very difficult problem; it requires the integrated application of syntactic, semantic, and pragmatic knowledge. This paper examines the hypothesis that instead of attempting …...
Knowledge extraction from machine-readable dictionaries: an evaluation
AbstractMachine-readable versions of everyday dictionaries have been seen as a likely source of information for use in natural language processing because they contain an enormous amount of lexical and semantic …...
Document Ranking Method for High Precision Rate
Many information retrieval(IR) systems retrieve relevant documents based on exact matching of keywords between a query and documents. This method degrades precision rate. In order to solve the problem, we …...
A Rule-Based and MT-Oriented Approach to Prepositional Phrase Attachment
I'rel)ositional l'hrase is the key issue in structuraJ ambiguity, l{ecently, re- searches in corpora provide the lexical cue of pre[)ositions with other words and the information could be used to …...
A Corpus-Based Approach for Building Semantic Lexicons
Semantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each applica- tion. Although some semantic information is available in general-purpose knowledge …...
Using the Web in Machine Learning for Other-Anaphora Resolution
We present a machine learning frame- work for resolving other-anaphora. Be- sides morpho-syntactic, recency, and se- mantic features based on existing lexi- cal knowledge resources, our algorithm obtains additional semantic …...
Parsing the Penn Chinese Treebank with Semantic Knowledge
. We build a class-based selection preference sub-model to in- corporate external semantic knowledge from two Chinese electronic se- mantic dictionaries. This sub-model is combined with modifier-head gen- eration sub-model. …...
EEG responds to conceptual stimuli and corpus semantics
Mitchell et al. (2008) demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI. This could be a very powerful technique for evaluating conceptual models …...
Abu-MaTran: automatic building of machine translation
Current Translation Memory (TM) sys- tems work at the surface level and lack se- mantic knowledge while matching. This paper presents an approach to incorpo- rating semantic knowledge in the …...
Encoding Semantic Resources in Syntactic Structures for Passage Reranking
In this paper, we propose to use seman- tic knowledge from Wikipedia and large- scale structured knowledge datasets avail- able as Linked Open Data (LOD) for the answer passage reranking …...
Is an Image Worth More than a Thousand Words? On the Fine-Grain Semantic Differences between Visual and Linguistic Representations
AbstractHuman concept representations are often grounded with visual information, yet some aspects of meaning cannot be visually represented or are better described with language. Thus, vision and language provide complementary …...
Using a Lexical Semantic Network for the Ontology Building
AbstractBuilding multilingual ontologies is a hard task as ontologies are often data-rich resources. We introduce an approach which allows exploiting structured lexical semantic knowledge for the ontology building. Given a …...
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English
AbstractTransformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate three models (BERT, …...
Does Chinese BERT Encode Word Structure?
AbstractContextualized representations give significantly improved results for a wide range of NLP tasks. Much work has been dedicated to analyzing the features captured by representative models such as BERT. Existing …...
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
AbstractRelation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unseen …...
Word associations and the distance properties of context-aware word embeddings
AbstractWhat do people know when they know the meaning of words? Word associations have been widely used to tap into lexical repre- sentations and their structure, as a way of …...
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining
AbstractRecently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources …...
Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency
AbstractIn this work, we explore the fitness of various word/concept representations in analyzing an experimental verbal fluency dataset providing human responses to 10 different category enumeration tasks. Based on human …...
KiPT: Knowledge-injected Prompt Tuning for Event Detection
AbstractEvent detection aims to detect events from the text by identifying and classifying event triggers (the most representative words). Most of the existing works rely heavily on complex downstream networks …...