Стемминг

This paper describes initial work on Deep Read, an automated reading comprehension system that accepts arbitrary text input (a story) and answers questions about it. We have acquired a corpus …...

Many natural language processes have some degree of preprocessing of data: tokenisation, stemming and so on. In the domain of Statisti- cal Machine Translation it has been shown that word …...

String transformation, which maps a source string s into its desirable form t∗, is related to various applications including stemming, lemmatization, and spelling correction. The essential and important step for …...

Stemming refers to the grouping of morphologi- cally related words into so-called stem classes for the purpose of improving information retrieval performance. Traditional approaches to stem- ming are language-specific and …...

AbstractA considerable amount of work has been put into development of stemmers and morphological analysers. The majority of these approaches use hand-crafted suffix-replacement rules but a few try to discover …...

We use an automatic pipeline of word tokenization, stemming, POS tagging, and vocalization to perform real-world Arabic dependency parsing. In spite of the high accuracy on the modules, the very …...

Started from the very beginning, Stemming has been playing significant roles in several Natural Language Processing Applications such as information retrieval (IR), machine translation (MT), morph analysis and deciding the …...

AbstractThis paper introduces a set of freely available, open-source tools for Turkish that are built around TRmorph, a morphological analyzer introduced earlier in Coltekin (2010). The article first provides an …...

We present labeled morphological segmentation—an alternative view of morphological processing that unifies sev- eral tasks. We introduce a new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmen- tation …...

In this paper we present an approach to re- duce data sparsity problems when translat- ing from morphologically rich languages into less inflected languages by selectively stemming certain word types. …...

AbstractPreprocessing is a preliminary step in many fields including IR and NLP. The effect of basic preprocessing settings on English for text summarization is well-studied. However, there is no such …...

We present several methods for stemming and lemmatization based on discrimina- tive string transduction. We exploit the paradigmatic regularity of semi-structured inflection tables to identify stems in an un- supervised …...

AbstractLanguage resources, such as corpora, are important for various natural language processing tasks. Urdu has millions of speakers around the world but it is under-resourced in terms of standard evaluation …...

In this paper we describe the complexity of building a lemmatizer for Arabic which has a rich and complex morphology, and show some differences between lemmatization and surface stemming, i.e. …...

With the growth of Internet usage, a massive amount of textual data is generated on social media and the Web. As the text on the Web are generated by different …...

In this paper, we conducted an aspect-based sentiment analysis using Google Maps user reviews of Indonesia’s tourism destinations which are Borobudur and Prambanan Temple. The aspects we used are Attractions, …...

Document Clustering aims to group similar documents based on the distance among them. A wide range of distance measures are available in the literature, and selecting an appropriate distance function …...

AbstractWith the ever-growing amount of textual data, extractive summarization has become increasingly crucial for efficiently processing information. The TextRank algorithm, a popular unsupervised method, offers excellent potential for this task. …...