8,629 research outputs found
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling
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
Do Multi-Sense Embeddings Improve Natural Language Understanding?
Learning a distinct representation for each sense of an ambiguous word could
lead to more powerful and fine-grained models of vector-space representations.
Yet while `multi-sense' methods have been proposed and tested on artificial
word-similarity tasks, we don't know if they improve real natural language
understanding tasks. In this paper we introduce a multi-sense embedding model
based on Chinese Restaurant Processes that achieves state of the art
performance on matching human word similarity judgments, and propose a
pipelined architecture for incorporating multi-sense embeddings into language
understanding.
We then test the performance of our model on part-of-speech tagging, named
entity recognition, sentiment analysis, semantic relation identification and
semantic relatedness, controlling for embedding dimensionality. We find that
multi-sense embeddings do improve performance on some tasks (part-of-speech
tagging, semantic relation identification, semantic relatedness) but not on
others (named entity recognition, various forms of sentiment analysis). We
discuss how these differences may be caused by the different role of word sense
information in each of the tasks. The results highlight the importance of
testing embedding models in real applications
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Minimally supervised induction of morphology through bitexts
textA knowledge of morphology can be useful for many natural language processing systems. Thus, much effort has been expended in developing accurate computational tools for morphology that lemmatize, segment and generate new forms. The most powerful and accurate of these have been manually encoded, such endeavors being without exception expensive and time-consuming. There have been consequently many attempts to reduce this cost in the development of morphological systems through the development of unsupervised or minimally supervised algorithms and learning methods for acquisition of morphology. These efforts have yet to produce a tool that approaches the performance of manually encoded systems.
Here, I present a strategy for dealing with morphological clustering and segmentation in a minimally supervised manner but one that will be more linguistically informed than previous unsupervised approaches. That is, this study will attempt to induce clusters of words from an unannotated text that are inflectional variants of each other. Then a set of inflectional suffixes by part-of-speech will be induced from these clusters. This level of detail is made possible by a method known as alignment and transfer (AT), among other names, an approach that uses aligned bitexts to transfer linguistic resources developed for one language–the source language–to another language–the target. This approach has a further advantage in that it allows a reduction in the amount of training data without a significant degradation in performance making it useful in applications targeted at data collected from endangered languages. In the current study, however, I use English as the source and German as the target for ease of evaluation and for certain typlogical properties of German. The two main tasks, that of clustering and segmentation, are approached as sequential tasks with the clustering informing the segmentation to allow for greater accuracy in morphological analysis.
While the performance of these methods does not exceed the current roster of unsupervised or minimally supervised approaches to morphology acquisition, it attempts to integrate more learning methods than previous studies. Furthermore, it attempts to learn inflectional morphology as opposed to derivational morphology, which is a crucial distinction in linguistics.Linguistic
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