Results 111 to 120 of about 29,472 (315)
Word Sense Disambiguation Focusing on POS Tag Disambiguation in Persian:
The present study deals with ambiguity at word level focusing on homographs. In different languages, homographs may cause ambiguity in text processing. In Persian, the number of homographs is high due to its orthographic structure as well as its complex ...
Elham Alayiaboozar+2 more
doaj
Toward Universal Word Sense Disambiguation Using Deep Neural Networks
Traditionally, approaches based on neural networks to solve the problem of disambiguation of the meaning of words (WSD) use a set of classifiers at the end, which results in a specialization in a single set of words-those for which they were trained ...
Hiram Calvo+3 more
doaj +1 more source
A Morphology-aware Network for Morphological Disambiguation [PDF]
Agglutinative languages such as Turkish, Finnish and Hungarian require morphological disambiguation before further processing due to the complex morphology of words. A morphological disambiguator is used to select the correct morphological analysis of a word.
arxiv
An Optimized Lesk-Based Algorithm for Word Sense Disambiguation
Computational complexity is a characteristic of almost all Lesk-based algorithms for word sense disambiguation (WSD). In this paper, we address this issue by developing a simple and optimized variant of the algorithm using topic composition in documents ...
Ayetiran Eniafe Festus, Agbele Kehinde
doaj +1 more source
Word Sense Disambiguation using Optimised Combinations of Knowledge Sources [PDF]
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowledge source. We describe a system which performs unrestricted word sense disambiguation (on all content words in free text) by combining different knowledge sources: semantic preferences, dictionary definitions and subject/domain codes along with part-of ...
arxiv
Word Sense Disambiguation Methods and Algorithms
This paper discuss various technique of word sense disambiguation. In WSD we disambiguate the correct sense of target word present in the text. WSD is a challenging field in the natural language processing, it helps in information retrieval, information extraction, machine learning.
openaire +1 more source
Abstract Two theories dominate the current debate over the nature of verbal irony: the pretence theory and the echoic theory. It is common ground in this debate that irony is sometimes both echoic and enacted through pretence; my concern here is with such cases.
Gregory Currie
wiley +1 more source
The Role of Conceptual Relations in Word Sense Disambiguation [PDF]
We explore many ways of using conceptual distance measures in Word Sense Disambiguation, starting with the Agirre-Rigau conceptual density measure. We use a generalized form of this measure, introducing many (parameterized) refinements and performing an exhaustive evaluation of all meaningful combinations.
arxiv
No Self‐Reference, No Ownership?
Abstract A ‘no‐ownership’ or ‘no‐self theory’ holds that there is no proper subject of experience; the ownership of experience can only be accounted for by invoking a sub‐personal entity. In the recent self‐versus‐no‐self debate, it is widely assumed that the no‐referent view of ‘I’, which is closely associated with Wittgenstein and G. E. M.
Bernhard Ritter
wiley +1 more source
A chain dictionary method for Word Sense Disambiguation and applications [PDF]
A large class of unsupervised algorithms for Word Sense Disambiguation (WSD) is that of dictionary-based methods. Various algorithms have as the root Lesk's algorithm, which exploits the sense definitions in the dictionary directly. Our approach uses the lexical base WordNet for a new algorithm originated in Lesk's, namely "chain algorithm for ...
arxiv