19,041 research outputs found
Limitations of Cross-Lingual Learning from Image Search
Cross-lingual representation learning is an important step in making NLP
scale to all the world's languages. Recent work on bilingual lexicon induction
suggests that it is possible to learn cross-lingual representations of words
based on similarities between images associated with these words. However, that
work focused on the translation of selected nouns only. In our work, we
investigate whether the meaning of other parts-of-speech, in particular
adjectives and verbs, can be learned in the same way. We also experiment with
combining the representations learned from visual data with embeddings learned
from textual data. Our experiments across five language pairs indicate that
previous work does not scale to the problem of learning cross-lingual
representations beyond simple nouns
Lexical Similarities and Differences in the Mathematics, Science and English Language Textbooks
The teaching of Science and Math in English in Malaysia is an area of great concern to educators and students alike. This study looks, in particular, at the common word classes among keywords identified in the Science, Math and English language Form One textbooks used in Malaysia and the differences in language use identified in the Science and Math textbooks
Recommended from our members
Simulating the Noun-Verb Asymmetry in the Productivity of Childrenâs Speech
Several authors propose that children may acquire syntactic categories on the basis of co-occurrence statistics of words in the input. This paper assesses the relative merits of two such accounts by assessing the type and amount of productive language that results from computing co-occurrence statistics over conjoint and independent preceding and following contexts. This is achieved through the implementation of these methods in MOSAIC, a computational model of syntax acquisition that produces utterances that can be directly compared to child speech, and has a developmental component (i.e. produces increasingly long utterances). It is shown that the computation of co-occurrence statistics over conjoint contexts or frames results in a pattern of productive speech that more closely resembles that displayed by language learning children. The simulation of the developmental patterning of childrenâs productive speech furthermore suggests two refinements to this basic mechanism: inclusion of utterance boundaries, and the weighting of frames for their lexical content
AudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis
Recently, sound recognition has been used to identify sounds, such as car and
river. However, sounds have nuances that may be better described by
adjective-noun pairs such as slow car, and verb-noun pairs such as flying
insects, which are under explored. Therefore, in this work we investigate the
relation between audio content and both adjective-noun pairs and verb-noun
pairs. Due to the lack of datasets with these kinds of annotations, we
collected and processed the AudioPairBank corpus consisting of a combined total
of 1,123 pairs and over 33,000 audio files. One contribution is the previously
unavailable documentation of the challenges and implications of collecting
audio recordings with these type of labels. A second contribution is to show
the degree of correlation between the audio content and the labels through
sound recognition experiments, which yielded results of 70% accuracy, hence
also providing a performance benchmark. The results and study in this paper
encourage further exploration of the nuances in audio and are meant to
complement similar research performed on images and text in multimedia
analysis.Comment: This paper is a revised version of "AudioSentibank: Large-scale
Semantic Ontology of Acoustic Concepts for Audio Content Analysis
Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation
Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources
Diacritic Restoration and the Development of a Part-of-Speech Tagset for the MÄori Language
This thesis investigates two fundamental problems in natural language processing: diacritic restoration and part-of-speech tagging. Over the past three decades, statistical approaches to diacritic restoration and part-of-speech tagging have grown in interest as a consequence of the increasing availability of manually annotated training data in major languages such as English and French. However, these approaches are not practical for most minority languages, where appropriate training data is either non-existent or not publically available. Furthermore, before developing a part-of-speech tagging system, a suitable tagset is required for that language. In this thesis, we make the following contributions to bridge this gap:
Firstly, we propose a method for diacritic restoration based on naive Bayes classifiers that act at word-level. Classifications are based on a rich set of features, extracted automatically from training data in the form of diacritically marked text. This method requires no additional resources, which makes it language independent. The algorithm was evaluated on one language, namely MÄori, and an accuracy exceeding 99% was observed.
Secondly, we present our work on creating one of the necessary resources for the development of a part-of-speech tagging system in MÄori, that of a suitable tagset. The tagset described was developed in accordance with the EAGLES guidelines for morphosyntactic annotation of corpora, and was the result of in-depth analysis of the MÄori grammar
- âŚ