No abstract

Due to the considerable growth of the volume of text documents on the Internet and in digital libraries, manual analysis of these documents is no longer feasible. Having efficient approaches to keyword extraction in order to retrieve the ‘key’ elements of the studied documents is now a necessity. Keyword extraction has been an active research field for many years, covering various applications in Text Mining, Information Retrieval, and Natural Language Processing, and meeting different requirements. However, it is not a unified domain of research. In spite of the existence of many approaches in the field, there is no single approach that effectively extracts keywords from different data sources. This shows the importance of having a comprehensive review, which discusses the complexity of the task and categorizes the main approaches of the field based on the features and methods of extraction that they use. This paper presents a general introduction to the field of keyword/keyphrase extraction. Unlike the existing surveys, different aspects of the problem along with the main challenges in the field are discussed. This mainly includes the unclear definition of ‘keyness’, complexities of targeting proper features for capturing desired keyness properties and selecting efficient extraction methods, and also the evaluation issues. By classifying a broad range of state-of-the-art approaches and analysing the benefits and drawbacks of different features and methods, we provide a clearer picture of them. This review is intended to help readers find their way around all the works related to keyword extraction and guide them in choosing or designing a method that is appropriate for the application they are targeting.

Terms are often supposed not to be prone to variation. Empirical observation of terms in various corpora (telecommunication, physics, medicine) shows, on the contrary, the quantitative and qualitative importance of term variation. We give a precise linguistic description of the rules relating to controlled terms and observed variants and of the constraints on these rules. This description leads to novel means of enriching terminologies via the generation of possible term variants or the simplification of nominal parse trees in order to discover potential variants.

We introduce, TermSuite, a JAVA and UIMA-based toolkit to build terminologies from corpora. TermSuite follows the classic two steps of terminology extraction tools, the identification of term candidates and their ranking, but implements new features. It is multilingually designed, scalable, and handles term variants. We focus on the main components: UIMA Tokens Regex for defining term and variant patterns over word annotations, and the grouping component for clustering terms and variants that works both at morphological and syntactic levels.

Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https://github.com/ygorg/KPTimes.

Current research in text mining favours the quantity of texts over their quality. But for bilingual terminology mining, and for many language pairs, large comparable corpora are not available. More importantly, as terms are defined vis-à-vis a specific domain with a restricted register, it is expected that the quality rather than the quantity of the corpus matters more in terminology mining. Our hypothesis, therefore, is that the quality of the corpus is more important than the quantity and ensures the quality of the acquired terminological resources. We show how important the type of discourse is as a characteristic of the comparable corpus.

Abstract. This article presents a method of extracting bilingual lexica composed of single-word terms (SWTs) and multi-word terms (MWTs) from comparable corpora of a technical domain. First, this method extracts MWTs in each language, and then uses statistical methods to align single words and MWTs by exploiting the term contexts. After explaining the difficulties involved in aligning MWTs and specifying our approach, we show the adopted process for bilingual terminology extraction and the resources used in our experiments. Finally, we evaluate our approach and demonstrate its significance, particularly in relation to non-compositional MWT alignment.

International audienceThe automatic compilation of bilingual lists of terms from specialized comparable corpora using lexical alignment has been successful for single-word terms (SWTs), but remains disappointing for multi-word terms (MWTs). The low frequency and the variability of the syntactic structures of MWTs in the source and the target languages are the main reported problems. This paper defines a general framework dedicated to the lexical alignment of MWTs from comparable corpora that includes a compositional translation process and the standard lexical context analysis. The compositional method which is based on the translation of lexical items being restrictive, we introduce an extended compositional method that bridges the gap between MWTs of different syntactic structures through morphological links. We experimented with the two compositional methods for the French–Japanese alignment task. The results show a significant improvement for the translation of MWTs and advocate further morphological analysis in lexical alignment