You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8 of its average POS tagging accuracy when trained at 1.2 of the total available training data, i.e. 150 sentences per language.

Cited Abstract(s):
[1]: Distributed word representations (word embeddings) have recently contributed to competitive performance in language modeling and several NLP tasks. In this work, we train word embeddings for more than 100 languages using their corresponding Wikipedias. We quantitatively demonstrate the utility of our word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages. We find their performance to be competitive with near state-of-art methods in English, Danish and Swedish. Moreover, we investigate the semantic features captured by these embeddings through the proximity of word groupings. We will release these embeddings publicly to help researchers in the development and enhancement of multilingual applications.
According to these findings, in order to avoid any hassles of tuning the hyperparameters of the word embedding model employed, we rather primarily use the publicly available pre-trained polyglot word embeddings of [1] without any task specific modification for our experiments. A key thing to note is that polyglot word embeddings are not tailored for any specific language analysis task such as POS tagging or NER. These word embeddings are instead trained in a manner favoring the word analogy task introduced by . The polyglot project distributes word embeddings for more than 100 languages. also report results on POS tagging, however, word representations they apply for these experiments are different from the task-agnostic representations they made publicly available.