2,472 research outputs found
Treebank-based multilingual unification-grammar development
Broad-coverage, deep unification grammar development is time-consuming and costly. This problem can be exacerbated
in multilingual grammar development scenarios. Recently (Cahill et al., 2002) presented a treebank-based methodology
to semi-automatically create broadcoverage, deep, unification grammar resources for English. In this paper we
present a project which adapts this model to a multilingual grammar development scenario to obtain robust, wide-coverage, probabilistic Lexical-Functional Grammars
(LFGs) for English and German via automatic f-structure annotation algorithms based on the Penn-II and TIGER
treebanks. We outline our method used to extract a probabilistic LFG from the TIGER treebank and report on the quality of the f-structures produced. We achieve an f-score of 66.23 on the evaluation of 100 random sentences against a manually constructed gold standard
The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations
The Parallel Meaning Bank is a corpus of translations annotated with shared,
formal meaning representations comprising over 11 million words divided over
four languages (English, German, Italian, and Dutch). Our approach is based on
cross-lingual projection: automatically produced (and manually corrected)
semantic annotations for English sentences are mapped onto their word-aligned
translations, assuming that the translations are meaning-preserving. The
semantic annotation consists of five main steps: (i) segmentation of the text
in sentences and lexical items; (ii) syntactic parsing with Combinatory
Categorial Grammar; (iii) universal semantic tagging; (iv) symbolization; and
(v) compositional semantic analysis based on Discourse Representation Theory.
These steps are performed using statistical models trained in a semi-supervised
manner. The employed annotation models are all language-neutral. Our first
results are promising.Comment: To appear at EACL 201
An integrated architecture for shallow and deep processing
We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis
A Dataset for Movie Description
Descriptive video service (DVS) provides linguistic descriptions of movies
and allows visually impaired people to follow a movie along with their peers.
Such descriptions are by design mainly visual and thus naturally form an
interesting data source for computer vision and computational linguistics. In
this work we propose a novel dataset which contains transcribed DVS, which is
temporally aligned to full length HD movies. In addition we also collected the
aligned movie scripts which have been used in prior work and compare the two
different sources of descriptions. In total the Movie Description dataset
contains a parallel corpus of over 54,000 sentences and video snippets from 72
HD movies. We characterize the dataset by benchmarking different approaches for
generating video descriptions. Comparing DVS to scripts, we find that DVS is
far more visual and describes precisely what is shown rather than what should
happen according to the scripts created prior to movie production
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