Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it can improve the performance of several tasks such as music recommendation, genre classification or artist similarity. However, most entity recognition systems in the music domain have concentrated on formal texts (e.g. artists' biographies, encyclopedic articles, etc.), ignoring rich and noisy user-generated content. In this work, we present a novel method to recognize musical entities in Twitter content generated by users following a classical music radio channel. Our approach takes advantage of both formal radio schedule and users' tweets to improve entity recognition. We instantiate several machine learning algorithms to perform entity recognition combining task-specific and corpus-based features. We also show how to improve recognition results by jointly considering formal and user-generated content
Named Entity Recognition (NER), or alternatively Named Entity Recognition and Classification (NERC), is the task of detecting entities in an input text and to assign them to a specific class. It starts to be defined in the early '80, and over the years several approaches have been proposed @cite_22 . Early systems were based on handcrafted rule-based algorithms, while recently several contributions by Machine Learning scientists have helped in integrating probabilistic models into NER systems.
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Named Entity Recognition (NER) is the task of locating and classifying names in text. In previous work, NER was limited to a small number of pre-defined entity classes (e.g., people, locations, and organizations). However, NER on the Web is a far more challenging problem. Complex names (e.g., film or book titles) can be very difficult to pick out precisely from text. Further, the Web contains a wide variety of entity classes, which are not known in advance. Thus, hand-tagging examples of each entity class is impractical. This paper investigates a novel approach to the first step in Web NER: locating complex named entities in Web text. Our key observation is that named entities can be viewed as a species of multiword units, which can be detected by accumulating n-gram statistics over the Web corpus. We show that this statistical method's F1 score is 50 higher than that of supervised techniques including Conditional Random Fields (CRFs) and Conditional Markov Models (CMMs) when applied to complex names. The method also outperforms CMMs and CRFs by 117 on entity classes absent from the training data. Finally, our method outperforms a semi-supervised CRF by 73 . This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of errors, are an indisputable key to progress. This paper presents a Named Entity Extraction (NEE) system for the CoNLL 2002 competition. The two main sub-tasks of the problem, recognition (NER) and classification (NEC), are performed sequentially and independently with separate modules. Both modules are machine learning based systems, which make use of binary AdaBoost classifiers. Named entity recognition (NER) is the task of segmenting and classifying occurrences of names in text. In NER, local contextual cues provide important evidence, but non-local information from the whole document could also prove useful: for example, it is useful to know that “Mary Kay Inc.” has been mentioned in a document to classify subsequent mentions of “Mary Kay” as an organization and not as a person. Previous works for NER typically model the problem as a sequence labeling problem, coupling the predictions of neighboring words with a Markov model such as conditional random fields. We propose applying the dual decomposition approach to combine a local sentential model and a non-local label consistency model for NER. The dual decomposition approach is a fusion approach which combines two models by constraining them to agree on their predictions on the test data. Empirically, we show that this approach outperforms the local sentential models on four out of five data sets. Named Entity Recognition (NER) is a subtask of information extraction and aims to identify atomic entities in text that fall into predefined categories such as person, location, organization, etc. Recent efforts in NER try to extract entities and link them to linked data entities. Linked data is a term used for data resources that are created using semantic web standards such as DBpedia. There are a number of online tools that try to identify named entities in text and link them to linked data resources. Although one can use these tools via their APIs and web interfaces, they use different data resources and different techniques to identify named entities and not all of them reveal this information. One of the major tasks in NER is disambiguation that is identifying the right entity among a number of entities with the same names; for example "apple" standing for both "Apple, Inc." the company and the fruit. We developed a similar tool called NERSO, short for Named Entity Recognition Using Semantic Open Data, to automatically extract named entities, disambiguating and linking them to DBpedia entities. Our disambiguation method is based on constructing a graph of linked data entities and scoring them using a graph-based centrality algorithm. We evaluate our system by comparing its performance with two publicly available NER tools. The results show that NERSO performs better. 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 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. We explore the use of Wikipedia as external knowledge to improve named entity recognition (NER). Our method retrieves the corresponding Wikipedia entry for each candidate word sequence and extracts a category label from the first sentence of the entry, which can be thought of as a definition part. These category labels are used as features in a CRF-based NE tagger. We demonstrate using the CoNLL 2003 dataset that the Wikipedia category labels extracted by such a simple method actually improve the accuracy of NER. 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. Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byproduct generate highly informative vector representations for words, known as word embeddings. In this paper we present two contributions: a new form of learning word embeddings that can leverage information from relevant lexicons to improve the representations, and the first system to use neural word embeddings to achieve state-of-the-art results on named-entity recognition in both CoNLL and Ontonotes NER. Our system achieves an F1 score of 90.90 on the test set for CoNLL 2003---significantly better than any previous system trained on public data, and matching a system employing massive private industrial query-log data. Named entity recognition (NER) is among SLU tasks that usually extract semantic information from textual documents. Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs. Such approach has some disadvantages (error propagation, metric to tune ASR systems sub-optimal in regards to the final task, reduced space search at the ASR output level,...) and it is known that more integrated approaches outperform sequential ones, when they can be applied. In this paper, we explore an end-to-end approach that directly extracts named entities from speech, though a unique neural architecture. On a such way, a joint optimization is possible for both ASR and NER. Experiments are carried on French data easily accessible, composed of data distributed in several evaluation campaigns. The results are promising since this end-to-end approach provides similar results (F-measure= 0.66 on test data) than a classical pipeline approach to detect named entity categories (F-measure=0.64). Last, we also explore this approach applied to semantic concept extraction, through a slot filling task known as a spoken language understanding problem, and also observe an improvement in comparison to a pipeline approach. We consider the problem of improving named entity recognition (NER) systems by using external dictionaries---more specifically, the problem of extending state-of-the-art NER systems by incorporating information about the similarity of extracted entities to entities in an external dictionary. This is difficult because most high-performance named entity recognition systems operate by sequentially classifying words as to whether or not they participate in an entity name; however, the most useful similarity measures score entire candidate names. To correct this mismatch we formalize a semi-Markov extraction process, which is based on sequentially classifying segments of several adjacent words, rather than single words. In addition to allowing a natural way of coupling high-performance NER methods and high-performance similarity functions, this formalism also allows the direct use of other useful entity-level features, and provides a more natural formulation of the NER problem than sequential word classification. Experiments in multiple domains show that the new model can substantially improve extraction performance over previous methods for using external dictionaries in NER. In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system's architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands). Named entity recognition NER is one of the basic tasks in automatic extraction of information from natural language texts. In this paper, we describe an automatic rule learning method that exploits different features of the input text to identify the named entities located in the natural language texts. Moreover, we explore the use of morphological features for extracting named entities from Turkish texts. We believe that the developed system can also be used for other agglutinative languages. The paper also provides a comprehensive overview of the field by reviewing the NER research literature. We conducted our experiments on the TurkIE dataset, a corpus of articles collected from different Turkish newspapers. Our method achieved an average F-score of 91.08 on the dataset. The results of the comparative experiments demonstrate that the developed technique is successfully applicable to the task of automatic NER and exploiting morphological features can significantly improve the NER from Turkish, an agglutinative language. Distantly supervised approaches have become popular in recent years as they allow training relation extractors without textbound annotation, using instead known relations from a knowledge base and a large textual corpus from an appropriate domain. While state of the art distant supervision approaches use off-theshelf named entity recognition and classification (NERC) systems to identify relation arguments, discrepancies in domain or genre between the data used for NERC training and the intended domain for the relation extractor can lead to low performance. This is particularly problematic for “non-standard” named entities such as album which would fall into the MISC category. We propose to ameliorate this issue by jointly training the named entity classifier and the relation extractor using imitation learning which reduces structured prediction learning to classification learning. We further experiment with Web features different features and compare against using two off-the-shelf supervised NERC systems, Stanford NER and FIGER, for named entity classification. Our experiments show that imitation learning improves average precision by 4 points over an one-stage classification model, while removing Web features results in a 6 points reduction. Compared to using FIGER and Stanford NER, average precision is 10 points and 19 points higher with our imitation learning approach. Two main challenges of Named Entity Recognition (NER) for tweets are the insufficient information in a tweet and the lack of training data. We propose a novel method consisting of three core elements: (1) normalization of tweets; (2) combination of a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model; and (3) semisupervised learning framework. The tweet normalization preprocessing corrects common ill-formed words using a global linear model. The KNN-based classifier conducts prelabeling to collect global coarse evidence across tweets while the CRF model conducts sequential labeling to capture fine-grained information encoded in a tweet. The semisupervised learning plus the gazetteers alleviate the lack of training data. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of normalization, KNN, and semisupervised learning. 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. This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phrases using a small number of labeled examples. Our method achieves 16.5 and 11.3 F-1 score improvement over co-training on disease and virus NER respectively. We also show that by adding candidate phrase embeddings as features in a sequence tagger gives better performance compared to using word embeddings. Named Entity Recognition (NER) plays an important role in a variety of online information management tasks including text categorization, document clustering, and faceted search. While recent NER systems can achieve near-human performance on certain documents like news articles, they still remain highly domain-specific and thus cannot effectively identify entities such as original technical concepts in scientific documents. In this work, we propose novel approaches for NER on distinctive document collections (such as scientific articles) based on n-grams inspection and classification. We design and evaluate several entity recognition features---ranging from well-known part-of-speech tags to n-gram co-location statistics and decision trees---to classify candidates. In addition, we show how the use of external knowledge bases (either specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic collections. We evaluate our system on two test collections created from a set of Computer Science and Physics papers and compare it against state-of-the-art supervised methods. Experimental results show that a careful combination of the features we propose yield up to 85 NER accuracy over scientific collections and substantially outperforms state-of-the-art approaches such as those based on maximum entropy. The challenges of Named Entities Recognition (NER) for tweets lie in the insufficient information in a tweet and the unavailability of training data. We propose to combine a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model under a semi-supervised learning framework to tackle these challenges. The KNN based classifier conducts pre-labeling to collect global coarse evidence across tweets while the CRF model conducts sequential labeling to capture fine-grained information encoded in a tweet. The semi-supervised learning plus the gazetteers alleviate the lack of training data. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of KNN and semi-supervised learning.