Graph-based Word Sense Disambiguation Research Articles

The impact of word sense disambiguation on stock price prediction

State-of-the-art decision support systems for stock price prediction incorporate pattern-based event detection in text into their predictions. These systems typically fail to account for word meaning, even though word sense disambiguation is crucial for text understanding. Therefore, we propose an advanced natural language processing pipeline for event-based stock price prediction, that allows for word sense disambiguation to be incorporated in the event detection process. We identify events in natural language news messages and subsequently weight these events for their historical impact on stock prices. We assess the merit of word sense disambiguation in event-based stock price prediction in two evaluation scenarios for NASDAQ-100 companies, based on historical stock prices and news articles retrieved from Dow Jones Newswires over a 2-year period. We evaluate the precision of generated buy and sell signals based on our predicted stock price movements, as well as the excess returns generated by a trading strategy that acts upon these signals. Event-based stock price predictions seem most reliable about 2 days into the future. The number of detected events tends to reduce with over 30% when graph-based word sense disambiguation using a degree centrality measure is applied in the event detection process, thus reducing the noise introduced into the stock price movement predictions by high-impact ambiguous events. As a result, modest improvements in the precision of buy and sell signals generated based on these predictions tend to lead to vast improvements of on average about 70% in the associated excess returns.

Context expansion approach for graph-based word sense disambiguation

Word sense disambiguation is a process to correctly identify the meanings of words in a given context. Being important in many natural language processing applications, this process is crucial in automatically understanding natural language expressions. Herein, we propose a variation of a well-known unsupervised graph-based word sense disambiguation method that utilizes all possible semantic information from a used lexical resource to increase graph-semantic connectivity for identifying the intended meanings of words in a given context. If the words have multiple potential meanings (senses) based on context, the proposed method builds an expanded graph representing most relevant semantic information of the words to be disambiguated. Nodes in the graph correspond to the context expansion set, which contains all associated information of each possible meaning of the word (word sense), and edges represent the semantic similarity between the expanded sets (nodes). Simultaneously, actual meaning is assigned to each target word using a locate graph centrality measure, which provides the degree of importance between graph nodes. Unlike most existing graph-based word sense disambiguation methods, wherein semantic relations (edges) between nodes are measured at the word level, the proposed method measures graph node semantic relations at the sentence level by expanding the words’ context, which contains all associated information for each possible word sense. Consequently, the proposed method can capture a higher degree of semantic information than existing approaches, thereby increasing semantic connectivity through a graph’s edges. Empirical results on benchmark datasets demonstrate that the proposed method outperforms all compared state-of-the-art graph-based word sense disambiguation approaches reported herein. We also report results obtained by applying the proposed method to a sentiment analysis task. These results demonstrate that the proposed method can determine the overall sentiment orientation of a given textual context.

Enhancing the performance of sentiment analysis task on product reviews by handling both local and global context

Commonly, product review analysis includes extracting sentiment from product documents. The contextual aspect contained in a review document has potential to improve results obtained by the sentiment analysis task. In this regard, this paper proposes an approach that takes into account both local and global context. The main contribution of this work is threefold. Firstly, local context is defined and the graph-based word sense disambiguation (WSD) method is extended to assign the correct sense of a word in the context of a sentence. Secondly, global context is defined for addressing contextual issues related to the specific domain of a review document by using an improved SentiCircle-based method. Thirdly, a weighted mean-based strategy to determine sentiment value at document level is presented. Several experiments were conducted to assess the proposed method. Overall, the proposed method outperformed the baseline method in the metrics of precision, recall, F-measure and accuracy.

Graph-based word sense disambiguation in Telugu language

Graph-based word sense disambiguation in Telugu language

Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration

Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration

PQAC-WN: constructing a wordnet for Pre-Qin ancient Chinese

The Princeton WordNet® (PWN) is a widely used lexical knowledge database for semantic information processing. There are now many wordnets under creation for languages worldwide. In this paper, we endeavor to construct a wordnet for Pre-Qin ancient Chinese (PQAC), called PQAC WordNet (PQAC-WN), to process the semantic information of PQAC. In previous work, most recently constructed wordnets have been established either manually by experts or automatically using resources from which translation pairs between English and the target language can be extracted. The former method, however, is time-consuming, and the latter method, owing to a lack of language resources, cannot be performed on PQAC. As a result, a method based on word definitions in a monolingual dictionary is proposed. Specifically, for each sense, kernel words are first extracted from its definition, and the senses of each kernel word are then determined by graph-based Word Sense Disambiguation. Finally, one optimal sense is chosen from the kernel word senses to guide the mapping between the word sense and PWN synset. In this research, we obtain 66 % PQAC senses that can be shared with English and another 14 % language-specific senses that were added to PQAC-WN as new synsets. Overall, the automatic mapping achieves a precision of over 85 %.

Predicate Matrix: automatically extending the semantic interoperability between predicate resources

This paper presents a novel approach to improve the interoperability between four semantic resources that incorporate predicate information. Our proposal defines a set of automatic methods for mapping the semantic knowledge included in WordNet, VerbNet, PropBank and FrameNet. We use advanced graph-based word sense disambiguation algorithms and corpus alignment methods to automatically establish the appropriate mappings among their lexical entries and roles. We study different settings for each method using SemLink as a gold-standard for evaluation. The results show that the new approach provides productive and reliable mappings. In fact, the mappings obtained automatically outnumber the set of original mappings in SemLink. Finally, we also present a new version of the Predicate Matrix, a lexical-semantic resource resulting from the integration of the mappings obtained by our automatic methods and SemLink.

An Iterative Approach to Graph-based Word Sense Disambiguation Using Word2Vec

An Iterative Approach to Graph-based Word Sense Disambiguation Using Word2Vec

A Graph-based Word Sense Disambiguation Using Measures of Graph Connectivity

어의 중의성 해소는 문맥에서 단어의 의미를 식별하는데 사용하는 자연 언어 처리 작업의 한부분이다. 사람의 언어는 많은 모호한 단어가 포함되어 있기 때문에 어의 중의성 해소의 목적은 단어의 정확한 의미를 파악하는 것이다. 이 논문에서는 어의 중의성 해소에 대한 비감독 그래프 기반의 방법을 설명하고, 단어의 정확한 의미를 찾기 위하여 그래프 연결성 구조를 사용한다. 이 논문의 알고리즘은 몇 개의 매개변수를 사용하며, 학습에 대한 의미 주석을 단 데이터가 필요하지 않다. 결과는 표준 데이터 셋에서 알고리즘의 성능을 평가하고, 최신 기술로 만족할만한 측정을 달성한 것을 보여주었다. 향후에 우리는 제안된 방법에 가중 그래프를 적용하는 것을 시도할 계획이다.

Distributionally Extended Network-based Word Sense Disambiguation in Semantic Clustering of Polish Texts

In the paper we present an extended version of the graph-based unsupervised Word Sense Disambiguation algorithm. The algorithm is based on the spreading activation scheme applied to the graphs dynamically built on the basis of the text words and a large wordnet. The algorithm, originally proposed for English and Princeton WordNet, was adapted to Polish and plWordNet. An extension based on the knowledge acquired from the corpus-derived Measure of Semantic Relatedness was proposed. The extended algorithm was evaluated against the manually disambiguated corpus. We observed improvement in the case of the disambiguation performed for shorter text contexts. In addition the algorithm application expressed improvement in document clustering task.

Graph Based Word Sense Disambiguation Method Using Distance Between Words

Almost all existing knowledge-based word sense disambiguation(WSD) methods used exploit context information contain,in certain window size around ambiguous word,are ineffective because all words in the window size have the same impact on determining the sense of ambiguous word.In order to solve the problem,this paper proposes a novel WSD model based on distance between words,which is built on the basics of traditional graph WSD model and can make full use of distance information.Through model reconstruction,optimization,parameter estimation and evaluation of comparison,the study demonstrates the feature of the new model: The words nearby ambiguous word will have more impact to the final sense of ambiguous word while the words far away from it will have less.Experimental results show that the proposed model can improve Chinese WSD performance,compared with the best evaluation results of SemEval-2007: task #5,this model gets MacroAve(macro-average accuracy) increase 3.1%.

Graph-based Word Sense Disambiguation of biomedical documents

Word Sense Disambiguation (WSD), automatically identifying the meaning of ambiguous words in context, is an important stage of text processing. This article presents a graph-based approach to WSD in the biomedical domain. The method is unsupervised and does not require any labeled training data. It makes use of knowledge from the Unified Medical Language System (UMLS) Metathesaurus which is represented as a graph. A state-of-the-art algorithm, Personalized PageRank, is used to perform WSD. When evaluated on the NLM-WSD dataset, the algorithm outperforms other methods that rely on the UMLS Metathesaurus alone. The WSD system is open source licensed and available from http://ixa2.si.ehu.es/ukb/. The UMLS, MetaMap program and NLM-WSD corpus are available from the National Library of Medicine https://www.nlm.nih.gov/research/umls/, http://mmtx.nlm.nih.gov and http://wsd.nlm.nih.gov. Software to convert the NLM-WSD corpus into a format that can be used by our WSD system is available from http://www.dcs.shef.ac.uk/∼marks/biomedical_wsd under open source license.

An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation

Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing. In this paper, we are concerned with graph-based algorithms for large-scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most "important" node among the set of graph nodes representing its senses. We introduce a graph-based WSD algorithm which has few parameters and does not require sense-annotated data for training. Using this algorithm, we investigate several measures of graph connectivity with the aim of identifying those best suited for WSD. We also examine how the chosen lexicon and its connectivity influences WSD performance. We report results on standard data sets and show that our graph-based approach performs comparably to the state of the art.

Semantic Drift in Espresso-style Bootstrapping: Graph-theoretic Analysis and Evaluation in Word Sense Disambiguation

Bootstrapping has a tendency, called semantic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of Espresso-style bootstrapping has the same root as the topic drift of Kleinberg's HITS, using a simplified graph-based reformulation of bootstrapping. We confirm that two graph-based algorithms, the von Neumann kernels and the regularized Laplacian, can reduce the effect of semantic drift in the task of word sense disambiguation (WSD) on Senseval-3 English Lexical Sample Task. Proposed algorithms achieve superior performance to Espresso and previous graph-based WSD methods, even though the proposed algorithms have less parameters and are easy to calibrate.