Product feature mining is a key subtask in fine-grained opinion mining. Previous works often use syntax constituents in this task. However, syntax-based methods can only use discrete contextual information, which may suffer from data sparsity. This paper proposes a novel product feature mining method which leverages lexical and contextual semantic clues. Lexical semantic clue verifies whether a...

Linguistic information can be conveyed in the form of speech and written text, but it is the content of the message that is ultimately essential for higher-level processes in language comprehension, such as making inferences and associations between text information and knowledge about the world. Linguistically, inference is the shovel that allows receivers to dig meaning out from the text with...

Contextual ontology helps in the knowledge-full indexing of documents, providing semantic structure to the document. The named entity and the associated keywords can be a clue to the contextual sense of the documents in the corpus. The proposed technique for the information retrieval creates the ecology of ontology which stores the ontological representation of the crawled documents. Further, t...

For Sinequa’s second participation to the Senseval evaluation, two systems using contextual semantic have been proposed. Based on different approaches, they both share the same data preprocessing and enrichment. The first system is a combined approach using semantic classification trees and information retrieval techniques. For the second system, the words from the context are considered as clu...

Reading contributes to vocabulary development and is regarded as an efficient pedagogical approach learning. Through reading, learners are exposed new words in their rightful context of use. This study investigates the effects input enhancements on incidental academic word learning through reading English texts among a Second Language (ESL) undergraduates. The compares different conditions, glo...

Pattern recognition technology has had a very important role in many fields of application including image processing, computer vision, remote sensing, etc. The advent of more powerful sensor systems should enable one to extract far more detailed information than ever before from observed data, but to realize this goal requires the development of concomitant data analysis techniques which can u...

Sentiment analysis requires human efforts to construct clue lexicons and/or annotations for machine learning, which are considered domaindependent. This paper presents a sentiment analysis method where clues are learned automatically with a minimum training data at a sentence level. The main strategy is to learn and weight sentiment-revealing clues by first generating a maximal set of candidate...

One of the fundamental questions for document analysis and subsequent automatic re-authoring solutions is the semantic and contextual integrity of the processed document. The problem is particularly severe in web document re-authoring as the segmentation process often creates an array of seemingly unrelated snippets of content without providing any concrete clue to aid the layout analysis proce...

introduction: mental sets are clues which are already coded and stored in memory, and retrieved when needed. the present study investigated the role of mental sets in solving anagram problems. methods: the design of this study is an experimental with repeated measurement design. the participants were 20 ma students and graduates chosen through voluntary sampling. each of them were required to u...

Morphosyntactic Disambiguation (Part of Speech tagging) is a useful benchmark problem for system comparison because it is typical for a large class of Natural Language Processing (NLP) problems that can be defined as disambiguation in local context. This paper adds to the literature on the systematic and objective evaluation of different methods to automatically learn this type of disambiguatio...