In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[EX Q]: We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges—these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges).  
 Question: How did they get relations between mentions?
[EX A]: Assign a value to the relation based on whether mentions occur in the same document, if mentions are identical, or if mentions are in the same coreference chain.

[EX Q]: The basic concept is to use a phone-discriminative model to produce frame-level phonetic features, and then use these features to enhance RNN LID systems that were originally built with raw acoustic features. The initial step is therefore feature combination, with the phonetic feature used as auxiliary information to assist acoustic RNN LID. This is improved further, as additional research identified that a simpler model using only the phonetic feature as the RNN LID input provides even better performance. We call this RNN model based on phonetic features the phonetic temporal neural LID approach, or PTN LID. As well as having a simplified model structure, the PTN offers deeper insight into the LID task by rediscovering the value of the phonetic temporal property in language discrimination.  
 Question: What is the main contribution of the paper? 
[EX A]: Proposing an improved RNN model, the phonetic temporal neural LID approach, based on phonetic features that results in better performance

[EX Q]: TextCat is the most well-known Perl implementation of the out-of-place method, it lists models for 76 languages in its off-the-shelf configuration; the program is not actively maintained. TextCat is the most well-known Perl implementation of the out-of-place method, it lists models for 76 languages in its off-the-shelf configuration; the program is not actively maintained. TextCat is not the only example of an off-the-shelf implementation of the out-of-place method: other implementations include libtextcat with 76 language models, JTCL with 15 languages, and mguesser with 104 models for different language-encoding pairs. The main issue addressed by later implementations is classification speed: TextCat is implemented in Perl and is not optimized for speed, whereas implementations such as libtextcat and mguesser have been specifically written to be fast and efficient. whatlang-rs uses an algorithm based on character trigrams and refers the user to the BIBREF7 article. It comes pre-trained with 83 languages.

is the language identifier embedded in the Google Chrome web browser. It uses a NB classifier, and script-specific classification strategies. assumes that all the input is in UTF-8, and assigns the responsibility of encoding detection and transcoding to the user. uses Unicode information to determine the script of the input. also implements a number of pre-processing heuristics to help boost performance on its target domain (web pages), such as stripping character sequences like .jpg. The standard implementation supports 83 languages, and an extended model is also available, that supports 160 languages.

is a Java library that implements a language identifier based on a NB classifier trained over character . The software comes with pre-trained models for 53 languages, using data from Wikipedia. makes use of a range of normalization heuristics to improve the performance on particular languages, including: (1) clustering of Chinese/Japanese/Korean characters to reduce sparseness; (2) removal of “language-independent” characters, and other text normalization; and (3) normalization of Arabic characters.

is a Python implementation of the method described by BIBREF150 , which exploits training data for the same language across multiple different sources of text to identify sequences of characters that are strongly predictive of a given language, regardless of the source of the text. This feature set is combined with a NB classifier, and is distributed with a pre-trained model for 97 languages prepared using data from 5 different text sources. BIBREF151 provide an empirical comparison of to , and and find that it compares favorably both in terms of accuracy and classification speed. There are also implementations of the classifier component (but not the training portion) of in Java, C, and JavaScript.

BIBREF153 uses a vector-space model with per-feature weighting on character sequences. One particular feature of is that it uses discriminative training in selecting features, i.e. it specifically makes use of features that are strong evidence against a particular language, which is generally not captured by NB models. Another feature of is that it uses inter-string smoothing to exploit sentence-level locality in making language predictions, under the assumption that adjacent sentences are likely to be in the same language. BIBREF153 reports that this substantially improves the accuracy of the identifier. Another distinguishing feature of is that it comes pre-trained with data for 1400 languages, which is the highest number by a large margin of any off-the-shelf system.

whatthelang is a recent language identifier written in Python, which utilizes the FastText NN-based text classification algorithm. It supports 176 languages.

implements an off-the-shelf classifier trained using Wikipedia data, covering 122 languages. Although not described as such, the actual classification algorithm used is a linear model, and is thus closely related to both NB and a cosine-based vector space model.

In addition to the above-mentioned general-purpose language identifiers, there have also been efforts to produce pre-trained language identifiers targeted specifically at Twitter messages. is a Twitter-specific tool with built-in models for 19 languages. It uses a document representation based on tries BIBREF401 . The algorithm is a LR classifier using all possible substrings of the data, which is important to maximize the available information from the relatively short Twitter messages.

BIBREF152 provides a comparison of 8 off-the-shelf language identifiers applied without re-training to Twitter messages. One issue they report is that comparing the accuracy of off-the-shelf systems is difficult because of the different subset of languages supported by each system, which may also not fully cover the languages present in the target data. The authors choose to compare accuracy over the full set of languages, arguing that this best reflects the likely use-case of applying an off-the-shelf system to new data. They find that the best individual systems are , and , but that slightly higher accuracy can be attained by a simple voting-based ensemble classifier involving these three systems.

In addition to this, commercial or other closed-source language identifiers and language identifier services exist, of which we name a few. The Polyglot 3000 and Lextek Language Identifier are standalone language identifiers for Windows. Open Xerox Language Identifier is a web service with available REST and SOAP APIs. 
 Question: what are the off-the-shelf systems discussed in the paper?
[EX A]:
Answer with content missing: (Names of many identifiers missing) TextCat, ChromeCLD, LangDetect, langid.py, whatlang, whatthelang, YALI, LDIG, Polyglot 3000, Lextek Language Identifier and Open Xerox Language Identifier.