Understanding idioms is important in NLP. In this paper, we study to what extent pre-trained BERT model can encode the meaning of a potentially idiomatic expression (PIE) in a certain context. We make use of a few existing datasets and perform two pr
obing tasks: PIE usage classification and idiom paraphrase identification. Our experiment results suggest that BERT indeed can separate the literal and idiomatic usages of a PIE with high accuracy. It is also able to encode the idiomatic meaning of a PIE to some extent.
Potentially idiomatic expressions (PIEs) are ambiguous between non-compositional idiomatic interpretations and transparent literal interpretations. For example, hit the road'' can have an idiomatic meaning corresponding to start a journey' or have a
literal interpretation. In this paper we propose a supervised model based on contextualized embeddings for predicting whether usages of PIEs are idiomatic or literal. We consider monolingual experiments for English and Russian, and show that the proposed model outperforms previous approaches, including in the case that the model is tested on instances of PIE types that were not observed during training. We then consider cross-lingual experiments in which the model is trained on PIE instances in one language, English or Russian, and tested on the other language. We find that the model outperforms baselines in this setting. These findings suggest that contextualized embeddings are able to learn representations that encode knowledge of idiomaticity that is not restricted to specific expressions, nor to a specific language.
The automatic recognition of idioms poses a challenging problem for NLP applications. Whereas native speakers can intuitively handle multiword expressions whose compositional meanings are hard to trace back to individual word semantics, there is stil
l ample scope for improvement regarding computational approaches. We assume that idiomatic constructions can be characterized by gradual intensities of semantic non-compositionality, formal fixedness, and unusual usage context, and introduce a number of measures for these characteristics, comprising count-based and predictive collocation measures together with measures of context (un)similarity. We evaluate our approach on a manually labelled gold standard, derived from a corpus of German pop lyrics. To this end, we apply a Random Forest classifier to analyze the individual contribution of features for automatically detecting idioms, and study the trade-off between recall and precision. Finally, we evaluate the classifier on an independent dataset of idioms extracted from a list of Wikipedia idioms, achieving state-of-the art accuracy.
The Master's stage has its importance in development and intellectual building.
However, in the process of applying for Master's studies, applicants must pass the English
Admission Test. When doing this test, master's students encounter difficultie
s in the area of
everyday English.
This research deals with the difficulties that master's students may face in the area of
everyday or spoken English. It is from the perspective of a sample of students doing a
language course that can help them do the final English admission test at the Higher
Institute of languages, Tishreen University. In order to achieve this goal, a questionnaire
has been designed to investigate into difficulties faced by those students in the area of
everyday English. After conducting the study and analysis, the research arrived at the
following results:
1- Master's students doing the English admission test suffer from difficulties in
idioms, the way they are written, and their use. In addition to that, there are anxieties when
dealing with phrasal verbs and sayings that reflect the cultural aspects of the English
society. Furthermore, there are problems with understanding English conversations.
2- There are no varieties with statistical significance in the area of everyday
English according to the sample of students in regard to the variables of gender and age.
3- Among the views of the sample of students, there exists no relation at a
significance in everyday English that can be attributed to the variable of the stage of
English acquisition.
يعد حقن البوتوكس لحل مشكلة التجاعيد الوجهية من أكثر الإجراءات التجميلية استخداماً في الولايات المتحدة
This paper presents a new method to recognize human face in different emotional situations. This method is based on proposed algorithm SD.R&C to discover skin and expression classification.
A group of 200 subjects was divided into five groups consisting of
dentist, students of the last year predoctoral dental, students of
pediatric dentistry, students of dental assistants, and pediatric
dentists. The groups were surveyed in order to
elicit their
responses to a series of ten simple line-drawn faces. Each person
was asked in a questionnaire to medicate on a five-point scale the
degree to which an adjective was appropriate or inappropriate for
each simple line-drawn face. Twenty adjectives were repeated for
the ten different faces. Means and standard deviations were
calculated for all responses. Results indicate that each face yielded
a unique set of appropriate adjectives as perceived by all subjects.
Beyond this basic agreement, however, group differences were
found in the selection of other appropriate adjectives. Based upon
a random subset of responses, discriminant analysis correctly
assigned group membership for 96% of the subjects. It was also
found that eight of the ten faces discriminated among the groups
with correct reclassification ranging from 88% to 96%.
