Language-aware Search

Generally it is possible to retrieve a list of topics specified by a user - entered keyword for a topic in a language - specific search engine.

At present, web searches do not take into account whether the search results are understandable to a particular user, especially when looking at students in an educational environment. Research (20) has tried to identify indicators that predict whether a text is understandable to readers of a certain level. At the same time, language awareness is one of the main objectives of educational search engines for students and teachers. Currently, current search engines support teachers by searching for the appropriate language to select suitable search terms such as English, Spanish, French, and English.

Context recognition plays a key role in future VR / AR application scenarios and this project can feed into the development of optimized VR and AR content. In order to determine the degree of attention of people during central learning tasks in VR or AR, this project uses two sensor modalities that can indicate attention: Electroencephalography signals (EEG), which measure the electrical activity of the brain, and eye trackers, which measure the viewing behaviour of the eyes. Information about the attention status of users can be of considerable benefit to both the user and the content provider during information consumption.

The motivating application of this project lies in the field of language learning and the development of VR / AR applications for the study of attention and attention status.

We try to make the semantic structure of the text explicit so that it can be more useful for us. More specifically, IE includes the ability to analyze text and identify semantically defined units and relationships. These relationships are recorded in order to search for specific relationships and derive additional information from explicitly stated facts.

Consider a simple model that uses linguistic features to identify the predominant gender of a text. Note that to build a useful database, IE needs to do much more than find sentences in the text: it needs to compute dates and times, resolve pronouns, identify event participants and properties, and so on.

I wrote a blog post examining the role of gender in news by determining whether men and women come from different contexts.

I applied a gender analysis of the text to an article in the New York Times, aiming to infer the influence of gender bias in the use of male and female words in news articles. Particularly interesting about this analysis was that I created a frequency-based score for masculinity and femininity. I found out whether male or female words appeared in more than 50% of articles, potentially reinforcing gender bias.

In reality, however, free texts have a lot of structure, except that most of them are not explicit, which makes it much more difficult to search and analyze the information in the text. This requires that the system understands enough to find relevant information and provide the right answers to the user's questions.

NLP can be used for the analysis of natural language, as it aims to derive meaningful and useful information from the text. L2 Body analysis and experimental work suggest that there is essentially no significant difference in structure between free and non-free texts.

Subsequent research, continued in 2007 at York and St. John's University, has examined and refined this model to improve knowledge of NLP as a tool for the study of language and language acquisition. This view was enriched by theoretical linguistics, sociolinguistics and psycholinguistics, as well as experimental and theoretical studies. We intend to apply this to applied linguistics, in particular to the analysis of designated languages such as English, French, German, Spanish and Chinese.

This new approach will be used to support the development of new approaches to the study of language and language acquisition as well as the application of NLP to a wide range of other areas.

Work three sheds light on the joint debate between socio-cultural work on language acquisition and education policy and practice. This claim is shared by the authors of a number of recent studies on the development of social cognition in English, and it has produced a wide range of new approaches to the study of language development and the application of NLP to socio-cultural work. In addition to raising teachers "awareness of the social interactions in which users of English develop soc iognitive resources, education policies and practices must overcome the deficiencies of monolithic thought models.

For example, the military is in the process of ensuring that workers at all levels of education understand all the manuals they need to read to do their jobs. We present a new search engine approach that allows teachers to search for texts in a variety of ways, with a range of readability measures for different literacy levels. The implemented prototype is based on state-of-the-art information technology and illustrates how we can integrate a series of "readability measures" in such a modular way.

Linguistic, SaaS, Search, NLP