Adaptive text mining: Inferring structure from sequences

Text mining is about inferring structure from sequences representing natural language text, and may be defined as the process of analyzing text to extract information that is useful for particular purposes. Although hand-crafted heuristics are a common practical approach for extracting information from text, a general, and generalizable, approach requires adaptive techniques. This paper studies the way in which the adaptive techniques used in text compression can be applied to text mining. It develops several examples: extraction of hierarchical phrase structures from text, identification of keyphrases in documents, locating proper names and quantities of interest in a piece of text, text categorization, word segmentation, acronym extraction, and structure recognition. We conclude that compression forms a sound unifying principle that allows many text mining problems to be tacked adaptively

Translating Phrases in Neural Machine Translation

Phrases play an important role in natural language understanding and machine translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is difficult to integrate them into current neural machine translation (NMT) which reads and generates sentences word by word. In this work, we propose a method to translate phrases in NMT by integrating a phrase memory storing target phrases from a phrase-based statistical machine translation (SMT) system into the encoder-decoder architecture of NMT. At each decoding step, the phrase memory is first re-written by the SMT model, which dynamically generates relevant target phrases with contextual information provided by the NMT model. Then the proposed model reads the phrase memory to make probability estimations for all phrases in the phrase memory. If phrase generation is carried on, the NMT decoder selects an appropriate phrase from the memory to perform phrase translation and updates its decoding state by consuming the words in the selected phrase. Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does. Experiment results on the Chinese to English translation show that the proposed model achieves significant improvements over the baseline on various test sets.Comment: Accepted by EMNLP 201

A Corpus-based Approach to the Chinese Word Segmentation

For a society based upon laws and reason, it has become too easy for us to believe that we live in a world without them. And given that our linguistics wisdom was originally motivated by the search for rules, it seems strange that we now consider these rules to be the exceptions and take exceptions as the norm. The current task of contemporary computational linguistics is to describe these exceptions. In particular, it suffices for most language processing needs, to just describe the argument and predicate within an elementary sentence, under the framework of local grammar. Therefore, a corpus-based approach to the Chinese Word Segmentation problem is proposed, as the first step towards a local grammar for the Chinese language. The two main issues with existing lexicon-based approaches are (a) the classification of unknown character sequences, i.e. sequences that are not listed in the lexicon, and (b) the disambiguation of situations where two candidate words overlap. For (a), we propose an automatic method of enriching the lexicon by comparing candidate sequences to occurrences of the same strings in a manually segmented reference corpus, and using methods of machine learning to select the optimal segmentation for them. These methods are developed in the course of the thesis specifically for this task. The possibility of applying these machine learning method will be discussed in NP-extraction and alignment domain. (b) is approached by designing a general processing framework for Chinese text, which will be called multi-level processing. Under this framework, sentences are recursively split into fragments, according to a language-specific, but domainindependent heuristics. The resulting fragments then define the ultimate boundaries between candidate words and therefore resolve any segmentation ambiguity caused by overlapping sequences. A new shallow semantical annotation is also proposed under the frame work of multi-level processing. A word segmentation algorithm based on these principles has been implemented and tested; results of the evaluation are given and compared to the performance of previous approaches as reported in the literature. The first chapter of this thesis discusses the goals of segmentation and introduces some background concepts. The second chapter analyses the current state-of-theart approach to Chinese language segmentation. Chapter 3 proposes a new corpusbased approach to the identification of unknown words. In chapter 4, a new shallow semantical annotation is also proposed under the framework of multi-level processing

How to Fine-Tune BERT for Text Classification?

Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets