Redefining part-of-speech classes with distributional semantic models

This paper studies how word embeddings trained on the British National Corpus interact with part of speech boundaries. Our work targets the Universal PoS tag set, which is currently actively being used for annotation of a range of languages. We experiment with training classifiers for predicting PoS tags for words based on their embeddings. The results show that the information about PoS affiliation contained in the distributional vectors allows us to discover groups of words with distributional patterns that differ from other words of the same part of speech. This data often reveals hidden inconsistencies of the annotation process or guidelines. At the same time, it supports the notion of `soft' or `graded' part of speech affiliations. Finally, we show that information about PoS is distributed among dozens of vector components, not limited to only one or two features

PowerAqua: fishing the semantic web

The Semantic Web (SW) offers an opportunity to develop novel, sophisticated forms of question answering (QA). Specifically, the availability of distributed semantic markup on a large scale opens the way to QA systems which can make use of such semantic information to provide precise, formally derived answers to questions. At the same time the distributed, heterogeneous, large-scale nature of the semantic information introduces significant challenges. In this paper we describe the design of a QA system, PowerAqua, designed to exploit semantic markup on the web to provide answers to questions posed in natural language. PowerAqua does not assume that the user has any prior information about the semantic resources. The system takes as input a natural language query, translates it into a set of logical queries, which are then answered by consulting and aggregating information derived from multiple heterogeneous semantic sources

The Relationship between Iranian EFL High School Students’ Multiple Intelligence Scores and Their Use of Learning Strategies

According to the theory of multiple intelligences (MI) propounded by Gardner (1983, 1999a, 1999b), each individual has a multitude of intelligences that are quite independent of each other and each individual has a unique cognitive profile. Having access to the MI profiles and learning strategies of learners could help the teachers in planning activities to connect both strategies and students’ talents and provide students with the best possible instruction. Thus, this study attempts to find out the relationship between the MI profiles and language learning strategies used by Iranian EFL high school students. Two hundred and twenty-nine students (121 males, 108 females) participated in the study. The instruments used to elicit information for this study were McKenzie’s (1999) MI inventory and the Strategy Inventory for Language Learning (SILL) Questionnaire. The findings revealed that there is a low, positive correlation between the two variables of MI and learning strategies, r = 0.24. In addition, it was found that there is a low, positive correlation between MI and different strategy types. The highest correlation was seen between meta-cognitive strategies and MI, followed by compensation and cognitive strategies. Furthermore, the findings reveal that Iranian students mostly use meta-cognitive strategies followed by social strategies