1,344 research outputs found
Empower Sequence Labeling with Task-Aware Neural Language Model
Linguistic sequence labeling is a general modeling approach that encompasses
a variety of problems, such as part-of-speech tagging and named entity
recognition. Recent advances in neural networks (NNs) make it possible to build
reliable models without handcrafted features. However, in many cases, it is
hard to obtain sufficient annotations to train these models. In this study, we
develop a novel neural framework to extract abundant knowledge hidden in raw
texts to empower the sequence labeling task. Besides word-level knowledge
contained in pre-trained word embeddings, character-aware neural language
models are incorporated to extract character-level knowledge. Transfer learning
techniques are further adopted to mediate different components and guide the
language model towards the key knowledge. Comparing to previous methods, these
task-specific knowledge allows us to adopt a more concise model and conduct
more efficient training. Different from most transfer learning methods, the
proposed framework does not rely on any additional supervision. It extracts
knowledge from self-contained order information of training sequences.
Extensive experiments on benchmark datasets demonstrate the effectiveness of
leveraging character-level knowledge and the efficiency of co-training. For
example, on the CoNLL03 NER task, model training completes in about 6 hours on
a single GPU, reaching F1 score of 91.710.10 without using any extra
annotation.Comment: AAAI 201
Switch-LSTMs for Multi-Criteria Chinese Word Segmentation
Multi-criteria Chinese word segmentation is a promising but challenging task,
which exploits several different segmentation criteria and mines their common
underlying knowledge. In this paper, we propose a flexible multi-criteria
learning for Chinese word segmentation. Usually, a segmentation criterion could
be decomposed into multiple sub-criteria, which are shareable with other
segmentation criteria. The process of word segmentation is a routing among
these sub-criteria. From this perspective, we present Switch-LSTMs to segment
words, which consist of several long short-term memory neural networks (LSTM),
and a switcher to automatically switch the routing among these LSTMs. With
these auto-switched LSTMs, our model provides a more flexible solution for
multi-criteria CWS, which is also easy to transfer the learned knowledge to new
criteria. Experiments show that our model obtains significant improvements on
eight corpora with heterogeneous segmentation criteria, compared to the
previous method and single-criterion learning
Enriching ontological user profiles with tagging history for multi-domain recommendations
Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites
Neural Word Segmentation with Rich Pretraining
Neural word segmentation research has benefited from large-scale raw texts by
leveraging them for pretraining character and word embeddings. On the other
hand, statistical segmentation research has exploited richer sources of
external information, such as punctuation, automatic segmentation and POS. We
investigate the effectiveness of a range of external training sources for
neural word segmentation by building a modular segmentation model, pretraining
the most important submodule using rich external sources. Results show that
such pretraining significantly improves the model, leading to accuracies
competitive to the best methods on six benchmarks.Comment: Accepted by ACL 201
A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging
In this paper, we propose a new approach to construct a system of
transformation rules for the Part-of-Speech (POS) tagging task. Our approach is
based on an incremental knowledge acquisition method where rules are stored in
an exception structure and new rules are only added to correct the errors of
existing rules; thus allowing systematic control of the interaction between the
rules. Experimental results on 13 languages show that our approach is fast in
terms of training time and tagging speed. Furthermore, our approach obtains
very competitive accuracy in comparison to state-of-the-art POS and
morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the
European Journal on Artificial Intelligence. Version 3: Resubmitted after
major revisions. Version 4: Resubmitted after minor revisions. Version 5: to
appear in AI Communications (accepted for publication on 3/12/2015
Apportioning Development Effort in a Probabilistic LR Parsing System through Evaluation
We describe an implemented system for robust domain-independent syntactic
parsing of English, using a unification-based grammar of part-of-speech and
punctuation labels coupled with a probabilistic LR parser. We present
evaluations of the system's performance along several different dimensions;
these enable us to assess the contribution that each individual part is making
to the success of the system as a whole, and thus prioritise the effort to be
devoted to its further enhancement. Currently, the system is able to parse
around 80% of sentences in a substantial corpus of general text containing a
number of distinct genres. On a random sample of 250 such sentences the system
has a mean crossing bracket rate of 0.71 and recall and precision of 83% and
84% respectively when evaluated against manually-disambiguated analyses.Comment: 10 pages, 1 Postscript figure. To Appear in Proceedings of the
Conference on Empirical Methods in Natural Language Processing, University of
Pennsylvania, May 199
- …