4,421 research outputs found
On the Effect of Semantically Enriched Context Models on Software Modularization
Many of the existing approaches for program comprehension rely on the
linguistic information found in source code, such as identifier names and
comments. Semantic clustering is one such technique for modularization of the
system that relies on the informal semantics of the program, encoded in the
vocabulary used in the source code. Treating the source code as a collection of
tokens loses the semantic information embedded within the identifiers. We try
to overcome this problem by introducing context models for source code
identifiers to obtain a semantic kernel, which can be used for both deriving
the topics that run through the system as well as their clustering. In the
first model, we abstract an identifier to its type representation and build on
this notion of context to construct contextual vector representation of the
source code. The second notion of context is defined based on the flow of data
between identifiers to represent a module as a dependency graph where the nodes
correspond to identifiers and the edges represent the data dependencies between
pairs of identifiers. We have applied our approach to 10 medium-sized open
source Java projects, and show that by introducing contexts for identifiers,
the quality of the modularization of the software systems is improved. Both of
the context models give results that are superior to the plain vector
representation of documents. In some cases, the authoritativeness of
decompositions is improved by 67%. Furthermore, a more detailed evaluation of
our approach on JEdit, an open source editor, demonstrates that inferred topics
through performing topic analysis on the contextual representations are more
meaningful compared to the plain representation of the documents. The proposed
approach in introducing a context model for source code identifiers paves the
way for building tools that support developers in program comprehension tasks
such as application and domain concept location, software modularization and
topic analysis
Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts
In this paper, we report a knowledge-based method for Word Sense
Disambiguation in the domains of biomedical and clinical text. We combine word
representations created on large corpora with a small number of definitions
from the UMLS to create concept representations, which we then compare to
representations of the context of ambiguous terms. Using no relational
information, we obtain comparable performance to previous approaches on the
MSH-WSD dataset, which is a well-known dataset in the biomedical domain.
Additionally, our method is fast and easy to set up and extend to other
domains. Supplementary materials, including source code, can be found at https:
//github.com/clips/yarnComment: 6 pages, 1 figure, presented at the 15th Workshop on Biomedical
Natural Language Processing, Berlin 201
Effect of heuristics on serendipity in path-based storytelling with linked data
Path-based storytelling with Linked Data on the Web provides users the ability to discover concepts in an entertaining and educational way. Given a query context, many state-of-the-art pathfinding approaches aim at telling a story that coincides with the user's expectations by investigating paths over Linked Data on the Web. By taking into account serendipity in storytelling, we aim at improving and tailoring existing approaches towards better fitting user expectations so that users are able to discover interesting knowledge without feeling unsure or even lost in the story facts. To this end, we propose to optimize the link estimation between - and the selection of facts in a story by increasing the consistency and relevancy of links between facts through additional domain delineation and refinement steps. In order to address multiple aspects of serendipity, we propose and investigate combinations of weights and heuristics in paths forming the essential building blocks for each story. Our experimental findings with stories based on DBpedia indicate the improvements when applying the optimized algorithm
Comparative Analysis of Word Embeddings for Capturing Word Similarities
Distributed language representation has become the most widely used technique
for language representation in various natural language processing tasks. Most
of the natural language processing models that are based on deep learning
techniques use already pre-trained distributed word representations, commonly
called word embeddings. Determining the most qualitative word embeddings is of
crucial importance for such models. However, selecting the appropriate word
embeddings is a perplexing task since the projected embedding space is not
intuitive to humans. In this paper, we explore different approaches for
creating distributed word representations. We perform an intrinsic evaluation
of several state-of-the-art word embedding methods. Their performance on
capturing word similarities is analysed with existing benchmark datasets for
word pairs similarities. The research in this paper conducts a correlation
analysis between ground truth word similarities and similarities obtained by
different word embedding methods.Comment: Part of the 6th International Conference on Natural Language
Processing (NATP 2020
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
Word embeddings are widely used in Nat-ural Language Processing, mainly due totheir success in capturing semantic infor-mation from massive corpora. However,their creation process does not allow thedifferent meanings of a word to be auto-matically separated, as it conflates theminto a single vector. We address this issueby proposing a new model which learnsword and sense embeddings jointly. Ourmodel exploits large corpora and knowl-edge from semantic networks in order toproduce a unified vector space of wordand sense embeddings. We evaluate themain features of our approach both qual-itatively and quantitatively in a variety oftasks, highlighting the advantages of theproposed method in comparison to state-of-the-art word- and sense-based models
Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness
We propose and study a novel supervised approach to learning statistical
semantic relatedness models from subjectively annotated training examples. The
proposed semantic model consists of parameterized co-occurrence statistics
associated with textual units of a large background knowledge corpus. We
present an efficient algorithm for learning such semantic models from a
training sample of relatedness preferences. Our method is corpus independent
and can essentially rely on any sufficiently large (unstructured) collection of
coherent texts. Moreover, the approach facilitates the fitting of semantic
models for specific users or groups of users. We present the results of
extensive range of experiments from small to large scale, indicating that the
proposed method is effective and competitive with the state-of-the-art.Comment: 37 pages, 8 figures A short version of this paper was already
published at ECML/PKDD 201
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