8,567 research outputs found
Sometimes less is more : Romanian word sense disambiguation revisited
Recent approaches to Word Sense Disambiguation (WSD) generally fall into two classes: (1) information-intensive approaches and (2) information-poor approaches. Our hypothesis is that for memory-based learning (MBL), a reduced amount of data is more beneficial than the full range of features used in the past. Our experiments show that MBL combined with a restricted set of features and a feature selection method that minimizes the feature set leads to competitive results, outperforming all systems that participated in the SENSEVAL-3 competition on the Romanian data. Thus, with this specific method, a tightly controlled feature set improves the accuracy of the classifier, reaching 74.0% in the fine-grained and 78.7% in the coarse-grained evaluation
Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution
This paper presents the use of probabilistic class-based lexica for
disambiguation in target-word selection. Our method employs minimal but precise
contextual information for disambiguation. That is, only information provided
by the target-verb, enriched by the condensed information of a probabilistic
class-based lexicon, is used. Induction of classes and fine-tuning to verbal
arguments is done in an unsupervised manner by EM-based clustering techniques.
The method shows promising results in an evaluation on real-world translations.Comment: 7 pages, uses colacl.st
Word sense disambiguation criteria: a systematic study
This article describes the results of a systematic in-depth study of the
criteria used for word sense disambiguation. Our study is based on 60 target
words: 20 nouns, 20 adjectives and 20 verbs. Our results are not always in line
with some practices in the field. For example, we show that omitting
non-content words decreases performance and that bigrams yield better results
than unigrams
Extending, trimming and fusing WordNet for technical documents
This paper describes a tool for the automatic
extension and trimming of a multilingual
WordNet database for cross-lingual retrieval
and multilingual ontology building in
intranets and domain-specific document
collections. Hierarchies, built from
automatically extracted terms and combined
with the WordNet relations, are trimmed
with a disambiguation method based on the
document salience of the words in the
glosses. The disambiguation is tested in a
cross-lingual retrieval task, showing
considerable improvement (7%-11%). The
condensed hierarchies can be used as
browse-interfaces to the documents
complementary to retrieval
Enriching very large ontologies using the WWW
This paper explores the possibility to exploit text on the world wide web in
order to enrich the concepts in existing ontologies. First, a method to
retrieve documents from the WWW related to a concept is described. These
document collections are used 1) to construct topic signatures (lists of
topically related words) for each concept in WordNet, and 2) to build
hierarchical clusters of the concepts (the word senses) that lexicalize a given
word. The overall goal is to overcome two shortcomings of WordNet: the lack of
topical links among concepts, and the proliferation of senses. Topic signatures
are validated on a word sense disambiguation task with good results, which are
improved when the hierarchical clusters are used.Comment: 6 page
Dependency relations as source context in phrase-based SMT
The Phrase-Based Statistical Machine Translation (PB-SMT) model has recently begun to include source context modeling, under the assumption that the proper lexical
choice of an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features such as words, parts-of-speech, and
supertags have been explored as effective source context in SMT. In this paper, we show that position-independent syntactic dependency relations of the head of a source phrase can be modeled as useful source context to improve target phrase selection and thereby improve overall performance of PB-SMT. On a DutchâEnglish translation task, by combining dependency relations and syntactic contextual features (part-of-speech), we achieved a 1.0 BLEU (Papineni et al., 2002) point improvement (3.1% relative) over the baseline
Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
This paper demonstrates that word sense disambiguation (WSD) can improve
neural machine translation (NMT) by widening the source context considered when
modeling the senses of potentially ambiguous words. We first introduce three
adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant
processes, and random walks, which are then applied to large word contexts
represented in a low-rank space and evaluated on SemEval shared-task data. We
then learn word vectors jointly with sense vectors defined by our best WSD
method, within a state-of-the-art NMT system. We show that the concatenation of
these vectors, and the use of a sense selection mechanism based on the weighted
average of sense vectors, outperforms several baselines including sense-aware
ones. This is demonstrated by translation on five language pairs. The
improvements are above one BLEU point over strong NMT baselines, +4% accuracy
over all ambiguous nouns and verbs, or +20% when scored manually over several
challenging words.Comment: To appear in TAC
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