為數眾多的漢語及英語非母語學習者常無法精準地控制句子中的句法和語意，然而，現今電腦輔助語言系統極少處理漢語學習者的句子錯誤，或者忽略某些常見的英語錯誤，且無法針對錯誤提供更進一步的描述，以致於學習者無法從錯誤中學習改進。藉此，本研究提出一創新的方法針對漢語學習者的錯誤句進行錯誤診斷，並針對英語學習者的錯誤句進行診斷和校正。
針對錯誤診斷，本論文提出懲罰式機率第一階歸納學習(penalized probabilistic First-Order Inductive Learning, pFOIL)演算法。pFOIL演算法整合歸納邏輯程式(Inductive Logic Programming, ILP)，第一階歸納學習(First-Order Inductive Learning, FOIL)以及懲罰式對數概似函數(Penalized Log-likelihood Function)，利用成堆的背景知識(Background Knowledge)表達那些可能是不確定、不完美甚至互相衝突的正確句與錯誤句的句子特色，進而推導產生出假設(Hypothesis)，形成漢語錯誤診斷模組，假設是由一堆類似人類可解讀的規則所組成，該規則提供了錯誤發生原因、錯誤發生的位置和錯誤形成的原因，而此規則除了可幫助學習者從錯誤中學習，還提供了重要線索協助進一步的錯誤句校正。針對漢語錯誤診斷，關係樣式背景知識(Relation Pattern Background Knowledge)和量化t分數背景知識(Quantized t-score Background Knowledge)被提出用於描述漢語句子的特色，應用在pFOIL 演算法中。關係樣式背景知識保留漢語句子中的形態、句法和語意資訊，由一或兩種形態、句法和語意屬性組合而成，而大多數的關係樣式背景知識是由相鄰的同種或兩種屬性所構成。量化t分數背景知識是從正確句計算各種成對屬性的t分數，再將量化後的t分數用來描述錯誤和正確的漢語句子。本論文同時提出解構式測試機制(Decomposition-based Diagnosis Mechanism)以便於處理一個錯誤句可能包含多個錯誤的情況，在進行錯誤診斷時，依序以各種可能造成該種錯誤類別的背景知識群組送入錯誤診斷模組中進行判別，直到所有錯誤類別相關的背景知識都被檢測後才結束該流程。
本研究進一步根據英語特性提出階層式架構進行英語錯誤句診斷和校正。在階層式架構中，英語錯誤診斷模組是由片語、子句以及句子各層次pFOIL 演算法各別推導出不同的假設所構成，接著，錯誤句校正則由多維度語言模組(Multidimensional Language Model)所負責。其中，為了便於各層次的pFOIL演算法能推導出適當的英語錯誤診斷模組，多類別背景知識(Multi-type Background Knowledge)和量化背景知識(Quantized Background Knowledge)被提出來用於表達英語句特色。由於英語句是以句法架構為基礎去延展句子，不同於漢語以語意或語用為基礎進行句子延伸，因此，多類別背景知識是以英語句法間的相依性和結構為基礎將英語句中的各類形態、句法及語意屬性加以組合。量化背景知識則是將從英語正確句中所計算出的t分數和語言模組機率加以量化，再將其量化值進一步用於描述所有英語正確和錯誤句。解構式測試機制同樣用於英語錯誤診斷，以便於抓取片語、子句甚至是句子中可能存在的多個錯誤。在進行英語錯誤句校正時，多維度語言模組不同於傳統由詞形為基本單位的語言模組，改以參考形態、句法和語意這些多維度值所構成的狀態(State)為語言模組的基本單元，並以bi-state計算語言模組機率值，參考pFOIL演算法診斷出的線索找出需要調整的狀態和相對應的值，進而產生出各種可能的英語錯誤句校正結果。
系統評估方面，《漢語病句辨析九百例》和Dr.Eye語料庫用於評估漢語錯誤句分類的效能，《中國學習者英語語料庫》用於英語錯誤句分類和校正的效能評估。三種常見的分類器：最大熵、C4.5和貝氏分類器為錯誤分類的基底比較系統。評估層面包括：pFOIL演算中各參數的影響，針對漢語和英語個別所提出的背景知識其效能和效益，和其他基底系統的比較，在各種錯誤類別的表現，以及以量化和質化方式評估英語錯誤句校正的表現。實驗結果顯示所提出的背景知識能有效減少模組建立時間並增進診斷效果，以pFOIL演算法為基礎的方法不論針對漢語或英語在各種錯誤類別診斷上都有相當穩定且精準的結果，多維度語言模組亦產生良好的英語錯誤校正結果。

Numerous Chinese and English non-native learners often have difficulty formulating correct syntactic and semantic structures themselves. Most of current computer-assisted language learning systems do not consider sentence errors by learners of Chinese as second language (CSL), ignore some common sentence errors by learners of English as second language (ESL), and cannot provide error description to help learners learn from errors. This dissertation focuses on the analysis of sentential, multiple grammatical, and local semantic errors as well as proposes a novel approach to error diagnosis of Chinese sentences and error diagnosis and correction of English sentences for CSL and ESL learners.
A penalized probabilistic First-Order Inductive Learning (pFOIL) algorithm is proposed for error diagnosis of Chinese and English sentences. The pFOIL algorithm integrates inductive logic programming (ILP), First-Order Inductive Learning (FOIL) and a penalized log-likelihood function for error diagnosis. This algorithm takes into account the uncertain, imperfect and conflicting characteristics of sentences to infer error types and generate human-interpretable rules to describe errors and further error correction. For Chinese error diagnosis, relation pattern background knowledge and quantized t-score background knowledge are proposed to characterize a Chinese sentence and then used for likelihood estimation in the pFOIL algorithm. The relation pattern background knowledge captures the morphological, syntactic and semantic attributes among the words in a Chinese sentence. One or two kinds of the extracted attributes are then integrated into a pattern to characterize a Chinese sentence. Most of relation pattern background knowledge is composed of two continuous attributes. The quantized t-score values are used to characterize various attributes of a Chinese sentence for quantized t-score background knowledge representation. Afterwards, a decomposition-based testing mechanism which decomposes a sentence into background knowledge set needed for each error type is proposed to infer all potential error types and causes of the sentence. With the pFOIL method, not only the error types but also the error causes and positions can be provided for non-native learners.
This dissertation further presents a hierarchical framework consisting of phrase, clause, and sentence levels for diagnosis and correction of English sentences for ESL learners. In the hierarchical framework, pFOIL algorithm is employed for English error diagnosis and then a multidimensional language model is proposed for English error correction. In English error diagnosis, the pFOIL approach is proposed to characterize a sentence using multi-type background knowledge which considers syntactic dependents and then captures the morphological, syntactic and semantic information among words, and quantized background knowledge with discrete values of multi-type information. A decomposition-based diagnosis mechanism is also employed to infer all potential error types and causes of the ESL sentence error. In English error correction, the multidimensional language model measuring the word associations in the diagnosed sentence considering the morphological, syntactic, and semantic information is used to correct the sentences.
The sentences from “Error Analysis of 900 Sample Sentences” and Dr.Eye corpus are selected for CSL error diagnosis performance evaluation, while Chinese learner English corpus (CLEC) is used for ESL error diagnosis and correction performance evaluation. Experimental results reveal that the pFOIL method outperforms the C4.5, maximum entropy and Naive Bayes classifiers in Chinese error classification. The evaluation on different ESL error classification experiments reveals that the proposed hierarchical pFOIL method outperforms the C4.5, Maximum Entropy and Naïve Bayes classifiers. The results also demonstrate that this multidimensional language model adopting morphological, syntactic, and semantic information can achieve a promising English correction performance.

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