scholarly journals Natural Language Inference for Arabic Using Extended Tree Edit Distance with Subtrees

2013 ◽  
Vol 48 ◽  
pp. 1-22 ◽  
Author(s):  
M. Alabbas ◽  
A. Ramsay
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Edit Distance ◽  
Arabic Text ◽  
Tree Edit Distance ◽  
Single Node ◽  
Preliminary Results ◽  
Standard Algorithm ◽  
Standard Tree

Many natural language processing (NLP) applications require the computation of similarities between pairs of syntactic or semantic trees. Many researchers have used tree edit distance for this task, but this technique suffers from the drawback that it deals with single node operations only. We have extended the standard tree edit distance algorithm to deal with subtree transformation operations as well as single nodes. The extended algorithm with subtree operations, TED+ST, is more effective and flexible than the standard algorithm, especially for applications that pay attention to relations among nodes (e.g. in linguistic trees, deleting a modifier subtree should be cheaper than the sum of deleting its components individually). We describe the use of TED+ST for checking entailment between two Arabic text snippets. The preliminary results of using TED+ST were encouraging when compared with two string-based approaches and with the standard algorithm.

scholarly journals A Preliminary Study for Building an Arabic Corpus of Pair Questions-texts from the Web: AQA-WebCorp

2016 ◽  
Vol 4 (2) ◽  
pp. 38
Author(s):  
Wided Bakari ◽  
Patrice Bellot ◽  
Mahmoud Neji
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Data Base ◽  
Machine Translation ◽  
Language Processing ◽  
Electronic Media ◽  
Preliminary Results ◽  
Preliminary Study ◽  
The Web

With the development of electronic media and the heterogeneity of Arabic data on the Web, the idea of building a clean corpus for certain applications of natural language processing, including machine translation, information retrieval, question answer, become more and more pressing. In this manuscript, we seek to create and develop our own corpus of pair’s questions-texts. This constitution then will provide a better base for our experimentation step. Thus, we try to model this constitution by a method for Arabic insofar as it recovers texts from the web that could prove to be answers to our factual questions. To do this, we had to develop a java script that can extract from a given query a list of html pages. Then clean these pages to the extent of having a data base of texts and a corpus of pair’s question-texts. In addition, we give preliminary results of our proposal method. Some investigations for the construction of Arabic corpus are also presented in this document.

scholarly journals Automatically Identifying the Source Words of Lexical Blends in English

2010 ◽  
Vol 36 (1) ◽  
pp. 129-149 ◽  
Author(s):  
Paul Cook ◽  
Suzanne Stevenson
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Statistical Model ◽  
Language Processing ◽  
Error Rate ◽  
Word Identification ◽  
Lexical Information ◽  
New Words ◽  
Preliminary Results ◽  
Source Word

Newly coined words pose problems for natural language processing systems because they are not in a system's lexicon, and therefore no lexical information is available for such words. A common way to form new words is lexical blending, as in cosmeceutical, a blend of cosmetic and pharmaceutical. We propose a statistical model for inferring a blend's source words drawing on observed linguistic properties of blends; these properties are largely based on the recognizability of the source words in a blend. We annotate a set of 1,186 recently coined expressions which includes 515 blends, and evaluate our methods on a 324-item subset. In this first study of novel blends we achieve an accuracy of 40% on the task of inferring a blend's source words, which corresponds to a reduction in error rate of 39% over an informed baseline. We also give preliminary results showing that our features for source word identification can be used to distinguish blends from other kinds of novel words.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence
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