5,564 research outputs found
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
Current approaches for service composition (assemblies of atomic services)
require developers to use: (a) domain-specific semantics to formalize services
that restrict the vocabulary for their descriptions, and (b) translation
mechanisms for service retrieval to convert unstructured user requests to
strongly-typed semantic representations. In our work, we argue that effort to
developing service descriptions, request translations, and matching mechanisms
could be reduced using unrestricted natural language; allowing both: (1)
end-users to intuitively express their needs using natural language, and (2)
service developers to develop services without relying on syntactic/semantic
description languages. Although there are some natural language-based service
composition approaches, they restrict service retrieval to syntactic/semantic
matching. With recent developments in Machine learning and Natural Language
Processing, we motivate the use of Sentence Embeddings by leveraging richer
semantic representations of sentences for service description, matching and
retrieval. Experimental results show that service composition development
effort may be reduced by more than 44\% while keeping a high precision/recall
when matching high-level user requests with low-level service method
invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on
Services Computing) on July 1
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
Interpretability of a predictive model is a powerful feature that gains the
trust of users in the correctness of the predictions. In word sense
disambiguation (WSD), knowledge-based systems tend to be much more
interpretable than knowledge-free counterparts as they rely on the wealth of
manually-encoded elements representing word senses, such as hypernyms, usage
examples, and images. We present a WSD system that bridges the gap between
these two so far disconnected groups of methods. Namely, our system, providing
access to several state-of-the-art WSD models, aims to be interpretable as a
knowledge-based system while it remains completely unsupervised and
knowledge-free. The presented tool features a Web interface for all-word
disambiguation of texts that makes the sense predictions human readable by
providing interpretable word sense inventories, sense representations, and
disambiguation results. We provide a public API, enabling seamless integration.Comment: In Proceedings of the the Conference on Empirical Methods on Natural
Language Processing (EMNLP 2017). 2017. Copenhagen, Denmark. Association for
Computational Linguistic
Name Disambiguation from link data in a collaboration graph using temporal and topological features
In a social community, multiple persons may share the same name, phone number
or some other identifying attributes. This, along with other phenomena, such as
name abbreviation, name misspelling, and human error leads to erroneous
aggregation of records of multiple persons under a single reference. Such
mistakes affect the performance of document retrieval, web search, database
integration, and more importantly, improper attribution of credit (or blame).
The task of entity disambiguation partitions the records belonging to multiple
persons with the objective that each decomposed partition is composed of
records of a unique person. Existing solutions to this task use either
biographical attributes, or auxiliary features that are collected from external
sources, such as Wikipedia. However, for many scenarios, such auxiliary
features are not available, or they are costly to obtain. Besides, the attempt
of collecting biographical or external data sustains the risk of privacy
violation. In this work, we propose a method for solving entity disambiguation
task from link information obtained from a collaboration network. Our method is
non-intrusive of privacy as it uses only the time-stamped graph topology of an
anonymized network. Experimental results on two real-life academic
collaboration networks show that the proposed method has satisfactory
performance.Comment: The short version of this paper has been accepted to ASONAM 201
Learning Relatedness Measures for Entity Linking
Entity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowl- edge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant enti- ties selected for annotation, since this minimizes errors in disambiguating entity-linking.
The definition of an e↵ective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high-quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of dif- ferent state-of-the-art entity-linking algorithms
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