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The journal provides a platform for original high-quality papers that deepen our understanding of the fundamental questions in these fields. Articles in NLPR are typically (slightly) longer than conference papers and focus on the methodological or theoretical implications of research. As such, the journal particularly welcomes articles that are strong in terms of their methodological set-up and/or grounding in theory. In line with this vision, solid evaluation and analyses of high quality are a minimal requirement for experimental papers, in addition to regular metrics of quality that apply to all papers (position, survey, theoretical and experimental) such as substance, originality and significance of the contribution. Targeted Authors Natural Language Processing Research (NLPR) is particularly interesting for authors who have work in renowned conferences and journals and who: need more space to, for example, outline theoretical foundations of their work, explain the details of a novel set-up or task, or to provide extensive (systematic) error analysis; want to publish a survey or literature overview with a vision of where the field is going (i.e. where more space is useful and a fast turnaround desirable); work at institutions where (high-quality) journal publications are valued over conference publications; cannot or prefer not to travel to (many) conferences; need a quick turnaround process for reviewing of work finished outside "conference season"; have work that provides important insights, but that does not directly improve the state-of-the-art and therefore prefer reviewers which are specifically instructed to value insights over numbers; have a paper that was recently rejected from a renowned conference (e.g. ACL, EMNLP or COLING) and want to submit an improved version to NLPR (provided that the original paper is extended to "journal length" and previous reviewer comments have been properly addressed to meet NLPR standards). Topics Covered Research areas covered in the journal include, but are not limited to, the following: Natural Language Processing (NLP) Grammar induction / Language models / Lemmatization / Morphological segmentation / POS tagging / Chunking / Parsing / Terminology extraction Lexical semantics / Logical semantics / Distributional and distributed semantics / Natural language understanding and generation / Recognizing textual entailment / Paraphrasing / Relationship extraction / Sentiment analysis / Topic segmentation and recognition / Linguistic theories / Cognitive modelling and psycholinguistics Automatic summarization / Co-reference resolution / Discourse analysis Phonology / Speech recognition / Speech segmentation / Text-to-speech / Speech processing and technology Machine Learning or Pattern Recognition Techniques for NLP Deep learning Statistical learning methods Bayesian networks Symbolic logic methods Applications Question answering Dialogue generation Social networks Agent communication Machine translation</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2020-12-12</div> </div> </div>  <div class="portlet" id="yw3"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-file"></i> Related Journals</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw4" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw4_c0">CCF</th><th id="yw4_c1">Full Name</th><th id="yw4_c2">Impact Factor</th><th id="yw4_c3">Publisher</th><th id="yw4_c4">ISSN</th></tr> </thead> <tbody> <tr class="odd"> <td></td><td><a href="/journal/743">European Journal of Operational Research</a></td><td>6.000</td><td>Elsevier</td><td>0377-2217</td></tr> <tr class="even"> <td></td><td><a href="/journal/900">International Journal of High Performance Computing Applications </a></td><td></td><td>SAGE</td><td>1094-3420</td></tr> <tr 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