Over the past 2 decades, shrimp aquaculture in Thailand has been impacted by white spot disease (WSD) caused by white spot syndrome virus (WSSV). Described here are results of a survey of 157 intensive shrimp farms in Chanthaburi province, Thailand, to identify potential farm management and location risk factors associated with the occurrence of WSD outbreaks. Logistic regression analysis of th...

Knowledge-based Word sense Disambiguation (WSD) methods heavily depend on knowledge. Therefore enriching knowledge is one of the most important issues in WSD. This paper proposes a novel idea of combining WordNet and ConceptNet for WSD. First, we present a novel method to automatically disambiguate the concepts in ConceptNet; and then we enrich WordNet with large amounts of semantic relations f...

Word Sense Disambiguation (WSD) is a task of identifying correct sense of a given word especially when it has multiple meanings. WSD acts as a foundation to many AI applications such as Data Mining, Information Retrieval and Machine Translation. It has drawn much interest in the last decade and much improved results are being obtained. For WSD we require a knowledge-base, using which we can res...

Statistical machine translation (SMT) systems use local cues from n-gram translation and language models to select the translation of each source word. Such systems do not explicitly perform word sense disambiguation (WSD), although this would enable them to select translations depending on the hypothesized sense of each word. Previous attempts to constrain word translations based on the result...

Word-sense disambiguation (WSD) is the process of finding the correct meaning of words that have multiple meanings. The unsupervised WSD algorithm is the type of WSD algorithm that leverages an external source of knowledge to guide the disambiguation process. The unsupervised WSD algorithm type is attracting more interest in the biomedical domain because of its implementation practicality, espe...

MOTIVATION Word Sense Disambiguation (WSD), automatically identifying the meaning of ambiguous words in context, is an important stage of text processing. This article presents a graph-based approach to WSD in the biomedical domain. The method is unsupervised and does not require any labeled training data. It makes use of knowledge from the Unified Medical Language System (UMLS) Metathesaurus w...

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is extreme importance in Arabic, as written words can be highly ambiguous; 43% diacritized have multiple interpretations and percentage increases 72% for non-diacritized words. Nevertheless, most Arabic text does not diacritical marks. Gloss-based WSD methods measure semantic similarity o...

We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-toEnglish SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task. Much effort has been put in designing and evaluating dedicated word sense disambiguation (WSD) m...

Word sense disambiguation (WSD) determines the correct meaning of a word that has more than one meaning, and is a critical step in biomedical natural language processing, as interpretation of information in text can be correct only if the meanings of their component terms are correctly identified first. Quality evaluation sets are important to WSD because they can be used as representative samp...

Ontology learning aims to automatically extract ontological concepts and relationships from related text repositories and is expected to be more efficient and scalable than manual ontology development. One of the challenging issues associated with ontology learning is word sense disambiguation (WSD). Most WSD research employs resources such as WordNet, text corpora, or a hybrid approach. Motiva...