Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions. Topics in natural language processing (NLP) similarly require annotated datasets to be used for such purposes. In this paper, we target at two NLP problems, named entity recognition and stance detection, and present the details of a tweet dataset in Turkish annotated for named entity and stance information. Within the course of the current study, both the named entity and stance annotations of the included tweets are made publicly available, although previously the dataset has been publicly shared with stance annotations only. We believe that this dataset will be useful for uncovering the possible relationships between named entity recognition and stance detection in tweets.
NER is a long-studied problem and its most common definition is the extraction of named entities such as person, location, and organization names from texts, in addition to some temporal and numeric expressions @cite_5 . Message Understanding Conference (MUC) series constitute an important milestone for NER research as they included NER competitions where related annotation guidelines, annotated datasets, and evaluation metrics were provided @cite_5 . The focus of recent NER studies has shifted from well-formed texts to social media texts including mostly microblog posts, as demonstrated in studies such as @cite_13 @cite_3 . Comprehensive surveys of NER research include @cite_18 @cite_12 @cite_2 .
@cite_18 @cite_3 @cite_2 @cite_5 @cite_13 @cite_12
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Named entity recognition (NER) is one of the main information extraction tasks and research on NER from Turkish texts is known to be rare. In this study, we present a rule-based NER system for Turkish which employs a set of lexical resources and pattern bases for the extraction of named entities including the names of people, locations, organizations together with time date and money percentage expressions. The domain of the system is news texts and it does not utilize important clues of capitalization and punctuation since they may be missing in texts obtained from the Web or the output of automatic speech recognition tools. The evaluation of the system is performed on news texts along with other genres encompassing child stories and historical texts, but as expected in case of manually engineered rule-based systems, it suffers from performance degradation on these latter genres of texts since they are distinct from the target domain of news texts. Furthermore, the system is evaluated on transcriptions of news videos leading to satisfactory results which is an important step towards the employment of NER during automatic semantic annotation of videos in Turkish. The current study is significant for its being the first rule-based approach to the NER task on Turkish texts with its evaluation on diverse text types. Named entity recognition (ner) for English typically involves one of three gold standards: muc, conll, or bbn, all created by costly manual annotation. Recent work has used Wikipedia to automatically create a massive corpus of named entity annotated text. We present the first comprehensive cross-corpus evaluation of ner. We identify the causes of poor cross-corpus performance and demonstrate ways of making them more compatible. Using our process, we develop a Wikipedia corpus which outperforms gold standard corpora on cross-corpus evaluation by up to 11 . Named Entity Recognition (NER) is a well-studied area in natural language processing (NLP) and the reported results in the literature are generally very high ( > 95) for most of the languages. Today, the focus area of most practical natural language applications (i.e. web mining, sentiment analysis, machine translation) is real natural language data such as Web2.0 or speech data. Nevertheless, the NER task is rarely investigated on this type of data which differs severely from formal written text. In this paper, we present 3 new Turkish data sets from different domains (on this focused area; namely from Twitter, a Speech-to-Text Interface and a Hardware Forum) annotated specifically for NER and report our first results on them. We believe, the paper draws light to the difficulty of these new domains for NER and the possible future work. Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, feature sparsity, and their effects on precision and recall. In particular, our findings indicate that NER approaches struggle to generalise in diverse genres with limited training data. Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire. Coupled with a higher incidence of unseen features more generally and the lack of large training corpora, this leads to significantly lower F1 scores for diverse genres as compared to more regular ones. We also find that leading systems rely heavily on surface forms found in training data, having problems generalising beyond these, and offer explanations for this observation.