Concept Of N-grams Research Articles

Keyphrase Extraction Using Enhanced Word and Document Embedding

ABSTRACT We live in a world where information is available, in all areas of human activity, increasingly in digital text documents. It is necessary to explore the knowledge implied in these documents considering its fast-growing availability. The use of keywords provides for a more effective search for a document of interest, as keywords highlight a document's primary concept and, therefore, allow the researcher's interest to be readily aligned with that text. In this article, an unsupervised keyword extraction approach is proposed. The proposed approach retrofitted the concept of n-grams with state-of-the-art words and document embeddings. The approach simultaneously proposed a new method to compose document vectors using important word vectors and their idf-scores. Here we use higher-order word n-grams to improve various unigram embeddings and introduce a novel task to produce document embedding for document representation. The performance of the proposed embeddings is evaluated using four different datasets. The combination of higher-order word n-grams retrofitted Glove, and document embedding is the best embedding to be used for extracting key phrases. The bi-gram retrofitted embedding improves the results significantly over the baseline approaches.

Unsupervised acquisition of idiomatic units of symbolic natural language: An n-gram frequency-based approach for the chunking of news articles and tweets.

Symbolic sequential data are produced in huge quantities in numerous contexts, such as text and speech data, biometrics, genomics, financial market indexes, music sheets, and online social media posts. In this paper, an unsupervised approach for the chunking of idiomatic units of sequential text data is presented. Text chunking refers to the task of splitting a string of textual information into non-overlapping groups of related units. This is a fundamental problem in numerous fields where understanding the relation between raw units of symbolic sequential data is relevant. Existing methods are based primarily on supervised and semi-supervised learning approaches; however, in this study, a novel unsupervised approach is proposed based on the existing concept of n-grams, which requires no labeled text as an input. The proposed methodology is applied to two natural language corpora: a Wall Street Journal corpus and a Twitter corpus. In both cases, the corpus length was increased gradually to measure the accuracy with a different number of unitary elements as inputs. Both corpora reveal improvements in accuracy proportional with increases in the number of tokens. For the Twitter corpus, the increase in accuracy follows a linear trend. The results show that the proposed methodology can achieve a higher accuracy with incremental usage. A future study will aim at designing an iterative system for the proposed methodology.

The integration of a newly defined N-gram concept and vector space model for documents ranking

Vector space model (VSM) is used in measuring the similarity between documents according to the frequency of common words among them. Furthermore, the N-gram concept is integrated in VSM to put into consideration the relation between common consecutive words in the documents. This approach does not consider the context and semantic dependency between nonconsecutive words existing in the same sentence. Accordingly, the approach proposed here presents a new definition of the N-gram concept as N non-consecutive words located in the same sentence, and utilises this definition in the VSM to enhance the measurement of the semantic similarity between documents. This approach measures and visualises the correlation between the words that are commonly existing together within the same sentence to enrich the analysis of domain experts. The results of the experimental work show the robustness of the proposed approach against the current ranking models.

A Mining Technique Using $N$n-Grams and Motion Transcripts for Body Sensor Network Data Repository

Recent years have witnessed a large influx of applications in the field of cyber-physical systems. An important class of these systems is body sensor networks (BSNs) where lightweight embedded processors and communication systems are tightly coupled with the human body. BSNs can provide researchers, care providers and clinicians access to tremendously valuable information extracted from data that are collected in users' natural environment. With this information, one can monitor the progression of a disease, identify its early onset, or simply assess user's wellness. One major obstacle is managing repositories that store the large amount of sensing data. To address this issue, we propose a data mining approach inspired by the experience in the areas of text and natural language processing. We represent sensor readings with a sequence of characters, called motion transcripts. Transcripts reduce complexity of the data significantly while maintaining morphological and structural properties of the physiological signals. To further take advantage of the physiological signal's structure, our data mining technique focuses on the characteristic transitions in the signals. These transitions are efficiently captured using the concept of n-grams. To facilitate a lightweight and fast mining approach, we reduce the overwhelmingly large number of n-grams via information gain (IG) feature selection. We report the effectiveness of the proposed approach in terms of the speed of mining while maintaining an acceptable accuracy in terms of the F-score combining both precision and recall.

AUTOMATIC ASSESSMENT OF STUDENTS' FREE-TEXT ANSWERS WITH DIFFERENT LEVELS

For improving the interaction between students and teachers, it is fundamental for teachers to understand students' learning levels. An intelligent computer system should be able to automatically evaluate students' answers when the teacher asks some questions. We first built the assessment corpus from the course in the university. With the corpus, we applied the following procedures to extract the relevant information and then built the feature model: (1) remove the punctuation and decimal numbers because it plays the noise roles, (2) apply the part-of-speech tagging such that the syntactic information is extracted, (3) for grouping the information, take the stemming and normalization procedure to sentences, and (4) extract other features. In this study, we treated the assessment problem as the classifying problem, and tried two kinds of classification strategies: two and three classifying classes. For two classes, we got an average of 66.3% precision rate at first. When adding n-gram concept to the feature model, the system reached to the average of 71.9% precision rate which increased performance by 5.6%. The same tendency emerged for three-class experiment. The experiments with SVM show exhilarating results and some improving efforts will be further made in the future.

Continuous query processing over music streams based on approximate matching mechanisms

It is foreseen that more and more music objects in symbolic format and multimedia objects, such as audio, video, or lyrics, integrated with symbolic music representation (SMR) will be published and broadcasted via the Internet. The SMRs of the flowing songs or multimedia objects will form a music stream. Many interesting applications based on music streams, such as interactive music tutorials, distance music education, and similar theme searching, make the research of content-based retrieval over music streams much important. We consider multiple queries with error tolerances over music streams and address the issue of approximate matching in this environment. We propose a novel approach to continuously process multiple queries over the music streams for finding all the music segments that are similar to the queries. Our approach is based on the concept of n-grams, and two mechanisms are designed to reduce the heavy computation of approximate matching. One mechanism uses the clustering of query n-grams to prune the query n-grams that are irrelevant to the incoming data n-gram. The other mechanism records the data n-gram that matches a query n-gram as a partial answer and incrementally merges the partial answers of the same query. We implement a prototype system for experiments in which songs in the MIDI format are continuously broadcasted, and the user can specify musical segments as queries to monitor the music streams. Experiment results show the effectiveness and efficiency of the proposed approach.

BLMT

Statistical analysis of amino acid and nucleotide sequences, especially sequence alignment, is one of the most commonly performed tasks in modern molecular biology. However, for many tasks in bioinformatics, the requirement for the features in an alignment to be consecutive is restrictive and "n-grams" (aka k-tuples) have been used as features instead. N-grams are usually short nucleotide or amino acid sequences of length n, but the unit for a gram may be chosen arbitrarily. The n-gram concept is borrowed from language technologies where n-grams of words form the fundamental units in statistical language models. Despite the demonstrated utility of n-gram statistics for the biology domain, there is currently no publicly accessible generic tool for the efficient calculation of such statistics. Most sequence analysis tools will disregard matches because of the lack of statistical significance in finding short sequences. This article presents the integrated Biological Language Modeling Toolkit (BLMT) that allows efficient calculation of n-gram statistics for arbitrary sequence datasets. BLMT can be downloaded from http://www.cs.cmu.edu/~blmt/source and installed for standalone use on any Unix platform or Unix shell emulation such as Cygwin on the Windows platform. Specific tools and usage details are described in a "readme" file. The n-gram computations carried out by the BLMT are part of a broader set of tools borrowed from language technologies and modified for statistical analysis of biological sequences; these are available at http://flan.blm.cs.cmu.edu/.