1,339 research outputs found
Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach
We design a new technique for the distributional semantic modeling with a
neural network-based approach to learn distributed term representations (or
term embeddings) - term vector space models as a result, inspired by the recent
ontology-related approach (using different types of contextual knowledge such
as syntactic knowledge, terminological knowledge, semantic knowledge, etc.) to
the identification of terms (term extraction) and relations between them
(relation extraction) called semantic pre-processing technology - SPT. Our
method relies on automatic term extraction from the natural language texts and
subsequent formation of the problem-oriented or application-oriented (also
deeply annotated) text corpora where the fundamental entity is the term
(includes non-compositional and compositional terms). This gives us an
opportunity to changeover from distributed word representations (or word
embeddings) to distributed term representations (or term embeddings). This
transition will allow to generate more accurate semantic maps of different
subject domains (also, of relations between input terms - it is useful to
explore clusters and oppositions, or to test your hypotheses about them). The
semantic map can be represented as a graph using Vec2graph - a Python library
for visualizing word embeddings (term embeddings in our case) as dynamic and
interactive graphs. The Vec2graph library coupled with term embeddings will not
only improve accuracy in solving standard NLP tasks, but also update the
conventional concept of automated ontology development. The main practical
result of our work is the development kit (set of toolkits represented as web
service APIs and web application), which provides all necessary routines for
the basic linguistic pre-processing and the semantic pre-processing of the
natural language texts in Ukrainian for future training of term vector space
models.Comment: In English, 9 pages, 2 figures. Not published yet. Prepared for
special issue (UkrPROG 2020 conference) of the scientific journal "Problems
in programming" (Founder: National Academy of Sciences of Ukraine, Institute
of Software Systems of NAS Ukraine
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for
clinical free-text that uses word and character n-gram embeddings. Our method
generates misspelling replacement candidates and ranks them according to their
semantic fit, by calculating a weighted cosine similarity between the
vectorized representation of a candidate and the misspelling context. To tune
the parameters of this model, we generate self-induced spelling error corpora.
We perform our experiments for two languages. For English, we greatly
outperform off-the-shelf spelling correction tools on a manually annotated
MIMIC-III test set, and counter the frequency bias of a noisy channel model,
showing that neural embeddings can be successfully exploited to improve upon
the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling
correction tool on manually annotated clinical records from the Antwerp
University Hospital, but can offer no empirical evidence that our method
counters the frequency bias of a noisy channel model in this case as well.
However, both our context-sensitive model and our implementation of the noisy
channel model obtain high scores on the test set, establishing a
state-of-the-art for Dutch clinical spelling correction with the noisy channel
model.Comment: Appears in volume 7 of the CLIN Journal,
http://www.clinjournal.org/biblio/volum
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
Uvid u automatsko izlučivanje metaforičkih kolokacija
Collocations have been the subject of much scientific research over the years. The focus of this research is on a subset of collocations, namely metaphorical collocations. In metaphorical collocations, a semantic shift has taken place in one of the components, i.e., one of the components takes on a transferred meaning. The main goal of this paper is to review the existing literature and provide a systematic overview of the existing research on collocation extraction, as well as the overview of existing methods, measures, and resources. The existing research is classified according to the approach (statistical, hybrid, and distributional semantics) and presented in three separate sections. The insights gained from existing research serve as a first step in exploring the possibility of developing a method for automatic extraction of metaphorical collocations. The methods, tools, and resources that may prove useful for future work are highlighted.Kolokacije su već dugi niz godina tema mnogih znanstvenih istraživanja. U fokusu ovoga istraživanja podskupina je kolokacija koju čine metaforičke kolokacije. Kod metaforičkih je kolokacija kod jedne od sastavnica došlo do semantičkoga pomaka, tj. jedna od sastavnica poprima preneseno značenje. Glavni su ciljevi ovoga rada istražiti postojeću literaturu te dati sustavan pregled postojećih istraživanja na temu izlučivanja kolokacija i postojećih metoda, mjera i resursa. Postojeća istraživanja opisana su i klasificirana prema različitim pristupima (statistički, hibridni i zasnovani na distribucijskoj semantici). Također su opisane različite asocijativne mjere i postojeći načini procjene rezultata automatskoga izlučivanja kolokacija. Metode, alati i resursi koji su korišteni u prethodnim istraživanjima, a mogli bi biti korisni za naš budući rad posebno su istaknuti. Stečeni uvidi u postojeća istraživanja čine prvi korak u razmatranju mogućnosti razvijanja postupka za automatsko izlučivanje metaforičkih kolokacija
- …