Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities

Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search. We link this problem to the classic problem of Chinese word segmentation and show the effectiveness of a tagging model based on Recurrent Neural Networks (RNNs) using characters as input. To compensate for the lack of training data, we propose a pre-training method on concatenated entity names in a large knowledge database. Pre-training improves the model by 33% and brings the sequence accuracy to 85%

Integrating Dictionary and Web N-grams for Chinese Spell Checking

Abstract Chinese spell checking is an important component of many NLP applications, including word processors, search engines, and automatic essay rating. Nevertheless, compared to spell checkers for alphabetical languages (e.g., English or French), Chinese spell checkers are more difficult to develop because there are no word boundaries in the Chinese writing system and errors may be caused by various Chinese input methods. In this paper, we propose a novel method for detecting and correcting Chinese typographical errors. Our approach involves word segmentation, detection rules, and phrase-based machine translation. The error detection module detects errors by segmenting words and checking word and phrase frequency based on compiled and Web corpora. The phonological or morphological typographical errors found then are corrected by running a decoder based on the statistical machine translation model (SMT). The results show that the proposed system achieves significantly better accuracy in error detection and more satisfactory performance in error correction than the state-of-the-art systems

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

Segmenting DNA sequence into words based on statistical language model

This paper presents a novel method to segment/decode DNA sequences based on n-gram statistical language model. Firstly, we find the length of most DNA “words” is 12 to 15 bps by analyzing the genomes of 12 model species. The bound of language entropy of DNA sequence is about 1.5674 bits. After building an n-gram biology languages model, we design an unsupervised ‘probability approach to word segmentation’ method to segment the DNA sequences. The benchmark of segmenting method is also proposed. In cross segmenting test, we find different genomes may use the similar language, but belong to different branches, just like the English and French/Latin. We present some possible applications of this method at last

Bilingually motivated domain-adapted word segmentation for statistical machine translation

We introduce a word segmentation approach to languages where word boundaries are not orthographically marked, with application to Phrase-Based Statistical Machine Translation (PB-SMT). Instead of using manually segmented monolingual domain-specific corpora to train segmenters, we make use of bilingual corpora and statistical word alignment techniques. First of all, our approach is adapted for the specific translation task at hand by taking the corresponding source (target) language into account. Secondly, this approach does not rely on manually segmented training data so that it can be automatically adapted for different domains. We evaluate the performance of our segmentation approach on PB-SMT tasks from two domains and demonstrate that our approach scores consistently among the best results across different data conditions

Fast and Accurate Neural Word Segmentation for Chinese

Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. This paper presents a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.Comment: To appear in ACL201