Shallow Natural Language Processing Techniques Research Articles

Situated natural language interaction in uncertain and open worlds

As intelligent robots become integrated into society, it becomes important for them to be capable of natural, human-like human-robot interaction (HRI). While there has been some progress on enabling natural-language based HRI (Mavridis, 2015), most natural language enabled robots rely on highly scripted interactions, keyword spotting, and shallow natural language processing techniques. For many applications, these methods may be sufficient to achieve the desired behavior, which may be restricted to a small class of tasks. Such methods, however, are not helpful for the development of robots that are generally and flexibly taskable, that can learn about new entities and concepts on the fly, and that are capable of engaging in truly natural human-like HRI.

Text plagiarism classification using syntax based linguistic features

The proposed work models document level text plagiarism detection as a binary classification problem, where the task is to distinguish a given suspicious-source document pair as plagiarized or non-plagiarized. The objective is to explore the potency of syntax based linguistic features extracted using shallow natural language processing techniques for plagiarism classification task. Shallow syntactic features, viz., part of speech tags and chunks are utilized after effective pre-processing and filtrations for pruning the irrelevant information. The work further proposes the modelling of this classification phase as an intermediate stage, which will be post candidate source retrieval and before exhaustive passage level detections. A two-phase feature selection approach is proposed, which improves the effectiveness of classification by selecting appropriate set of features as the input to machine learning based classifiers. The proposed approach is evaluated on smaller and larger test conditions using the corpus of plagiarized short answers (PSA) and plagiarism instances collected from PAN corpus respectively. Under both the test conditions, performances are evaluated using general as well as advanced classification metrics. Another main contribution of the current work is the analysis of dependencies and impact of the extracted features, upon the type and complexity of plagiarism imposed in the documents. The proposed results are compared with the two state-of-the-art approaches and they outperform the baseline approaches significantly. This in turn reflects the cogency of syntactic linguistic features in document level plagiarism classification, especially for the instances close to manual or real plagiarism scenarios.

P271 Computerized Guidelines Analysis: 5-Years Experience And User Feedback

BackgroundIn 2007, HAS (Haute Autorité de Santé, France) started experimenting the use of computerised guidelines analysis with the G-DEE software as part of the guidelines production process. G-DEE uses shallow...

An Expert System for Automated Essay Scoring (AES) in Computing using Shallow NLP Techniques for Inferencing

In this paper, we present the report of the development of an Expert System (ES) that; acquires the knowledge of Subject Matter Experts (SMEs) in a specific computing field, “Software Engineering”, uses a built-in Inference Engine designed with Shallow Natural Language Processing Techniques (Information Extraction using Tokenization, Statistical Keyword Analysis and Domain-Specific Dictionary) and a Fuzzy-Scoring Model to assess Students’ Free-Text Answers to Open-Ended Questions and hence, computes the correctness of students’ answers with respect to lecturers’ underlying model answers or templates. The newly developed ES was adapted to an academic course in the University System and its performance was evaluated using certain Statistical Metrics. The results from the evaluation were compared with existing Automated Essay Scoring Systems (AESs) using certain thresholds and conclusions were drawn.

Constraint-based Ontology Induction from Online Customer Reviews

We present an unsupervised, domain-independent technique for inducing a product-specific ontology of product features based upon online customer reviews. We frame ontology induction as a logical assignment problem and solve it with a bounds consistency constrained logic program. Using shallow natural language processing techniques, reviews are parsed into phrase sequences where each phrase refers to a single concept. Traditional document clustering techniques are adapted to collect phrases into initial concepts. We generate a token graph for each initial concept cluster and find a maximal clique to define the corresponding logical set of concept sub-elements. The logic program assigns tokens to clique sub-elements. We apply the technique to several thousand digital camera customer reviews and evaluate the results by comparing them to the ontologies represented by several prominent online buying guides. Because our results are drawn directly from customer comments, differences between our automatically induced product features and those in extant guides may reflect opportunities for better managing customer-producer relationships rather than errors in the process.

A Dialogue-Based Information Retrieval Assistant Using Shallow NLP Techniques in Online Sales Domains

Most of commercial websites provide customers with menu-driven navigation and keyword search. However, these inconvenient interfaces increase the number of mouse clicks and decrease customers' interest in surfing the websites. To resolve the problem, we propose an information retrieval assistant using a natural language interface in online sales domains. The information retrieval assistant has a client-server structure; a system connector and a NLP (natural language processing) server. The NLP server performs a linguistic analysis of users' queries with the help of coordinated NLP agents that are based on shallow NLP techniques. After receiving the results of the linguistic analysis from the NLP server, the system connector interacts with outer information provision systems such as conventional information retrieval systems and relational database management systems according to the analysis results. Owing to the client-server structure, we can easily add other information provision systems to the information retrieval assistant with trivial modifications of the NLP server. In addition, the information retrieval assistant guarantees fast responses because it uses shallow NLP techniques. In the preliminary experiment, as compared to the menu-driven system, we found that the information retrieval assistant could reduce the bothersome tasks such as menu selecting and mouse clicking because it provides a convenient natural language interface.

Genre-Based In-Document Content Type Classification

This paper presents an in-document content classification approach that combines genre analysis and shallow natural language processing techniques to do document segment-level content classification. Given a document in a particular genre, we can classify the content of each segment (e.g. a paragraph) based on the recognized content type and typical linguistic features of the genre. The informal evaluative document genre is chosen as the test genre, and the online consumer review is used as the test data set. The classification results support our hypothesis that the content type of segment in a document of a particular genre could be predicted from the linguistic features. This approach may be used as a component in faceted search, multi-document summarization and many other information processing applications.