1,136 research outputs found
Polyglot: Distributed Word Representations for Multilingual NLP
Distributed word representations (word embeddings) have recently contributed
to competitive performance in language modeling and several NLP tasks. In this
work, we train word embeddings for more than 100 languages using their
corresponding Wikipedias. We quantitatively demonstrate the utility of our word
embeddings by using them as the sole features for training a part of speech
tagger for a subset of these languages. We find their performance to be
competitive with near state-of-art methods in English, Danish and Swedish.
Moreover, we investigate the semantic features captured by these embeddings
through the proximity of word groupings. We will release these embeddings
publicly to help researchers in the development and enhancement of multilingual
applications.Comment: 10 pages, 2 figures, Proceedings of Conference on Computational
Natural Language Learning CoNLL'201
Towards a machine-learning architecture for lexical functional grammar parsing
Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also
recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages.
The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing.
In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able
to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously
lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages
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