Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains (2018)

Abstract: The meaning of a sentence in a document is more easily determined if its constituent words exhibit cohesion with respect to their individual semantics. This paper explores the degree of cohesion among a document’s words using lexical chains as a semantic representation of its meaning. Using a combination of diverse types of lexical chains, we develop a text document representation that can be used for semantic document retrieval. For our approach, we develop two kinds of lexical chains: (i) a multilevel flexible chain representation of the extracted semantic values, which is used to construct a fixed segmentation of these chains and constituent words in the text; and (ii) a fixed lexical chain obtained directly from the initial semantic representation from a document. The extraction and processing of concepts is performed using WordNet as a lexical database. The segmentation then uses these lexical chains to model the dispersion of concepts in the document. Representing each document as a high-dimensional vector, we use spherical k-means clustering to demonstrate that our approach performs better than previous techniques.

## introduction since the late 1980's, when there was a burst of research in dimensional reduction techniques @xcite , information retrieval has been concerned with semantics. since multimedia entities, including text, have multiple meanings, examining the context in which they appear became of significant importance to their overall disambiguation. an important example of this in the natural language processing community was the formulation of lexical chains @xcite . a lexical chain is a contiguous portion of text which has semantic cohesion. such chains are of variable length and have been used throughout the intervening years in many ways; e.g., for semantic characterization of the underlying document, for question and answers tasks, for document summarization, and for clustering documents into semantically uniform groups. in this paper, we propose two new types of lexical chains based on semantic representation: the first is called flexible-to-fixed lexical chains (flex2fix) and the second, fixed lexical chains (fixlc). in the first, these representations follow and extend the model proposed by @xcite , transforming their flexible lexical chains into fixed structures, which are later transformed into vectors of semantic values. in the second, we build fixed lexical structures directly from their initial semantic value. first, we start by identifying the most suitable semantic representation for each word, considering their context. second, we use these semantic abstractions and find the flexible lexical chains in a document. this approach extracts and builds cohesive sequences of ideas with respect to the semantic value shared among words in a dynamic way. third, we develop an approach to transform flexible lexical chains into fixed lexical chains. all these chains are used to construct a vector representation corresponding to a document's semantic structure. this is done to represent the document's semantic value at a higher level of abstraction. we also investigate how fixed lexical chains obtained directly from the document's semantic representation perform against traditional approaches (e.g. bag-of-words (bow)) and the derived fixed structures from the flexible ones. the remainder of this paper appears as follows. section 2 reviews existing work in lexical chains and provides additional information on our technique. in section 3, we present our methodology and proposed algorithms for content-based retrieval using lexical chains. section approach, while in section 5, we offer some final considerations and potential future work. ## related work the term lexical chains was first proposed by @xcite as an extension of lexical cohesion, a concept introduced by @xcite . a text in which many of its words are semantically connected often produces a certain degree of continuity in its ideas, providing good cohesion among its words. lexical cohesion is more likely to occur between words close to each other in a text, especially those contiguously ordered. the sequence of related words, tied by a common semantic affinity is classified as a lexical chain @xcite . the use of lexical chains in document processing and analysis (e.g. text similarity, word disambiguation, document clustering) has been widely studied in the literature. in @xcite , lexical chains are used to summarize texts. the former extracts and classifies lexical chains and discovers significant sentences to represent documents from them. the latter proposes a linear-time algorithm for constructing the lexical chains that will capture the meaning of a text. some authors use wordnet (wn) to improve the search and evaluation of lexical chains. @xcite compare several measurements of semantic distance and relatedness using lexical chains in conjunction with wn. @xcite studies the use of lexical chains for finding topically related words. this is done considering the glosses for each synset in wn. @xcite explores the benefits of using wn to improve document clustering based on an explicit matching between terms found in the text and the lexical database. @xcite presents a methodology to categorize and find the most predominant synsets in untagged texts using wn. in @xcite , wn is used for document clustering, exploring the benefits of incorporating hypernyms and synonyms into their approach. in @xcite , an application developed in perl is proposed to calculate the relatedness of concepts via wn through different measures of similarity. @xcite hypothesizes that if the semantics of words are known in advance, it is possible to get a better statistical inference concerning a document's overall idea. in more recent works, @xcite presents an extensive study in the word sense disambiguation (wsd) arena, in which he proposes an unsupervised wsd algorithm based on generating spreading activation networks (sans) from senses of a thesaurus and the relations between them. @xcite explores the theory behind state-of-the-art techniques for semantic similarity measures in four main categories: path length-based, information contentbased, feature-based, and hybrid measures. (alagha and nafee, 2014) proposes an approach to improve document clustering by exploring the semantic knowledge offered by wikipedia. the authors discuss this hypothesis, comparing the results using wn and wikipedia, claiming that the latter is more robust. in @xcite several measures of similarity (e.g. normalized google distance, normalized compression distance, cosine distance, latent semantic similarity) are applied to categorize sentences, words, paragraphs and documents according to their lexical and semantic similarities. in @xcite an extensive study about available text similarity measures is done as part of semantic evaluation, and for the detection of text reusability. they argue that text similarity cannot be considered as a static and absolute notion. instead, one should carefully define in which levels and perspectives two documents are similar or not. @xcite combines lexical chains and wn to extract a set of semantically related words from texts and then uses them for clustering. their approach uses an ontological hierarchical structure and relations to provide a more accurate assessment of the similarity between terms for wsd. in @xcite , they conduct a comprehensive review of the methods related to xml-based semistructured semantic analysis and disambiguation. although focused in the xml arena, this work provides an overview of the semantic disambiguation field, as well. they cover traditional wsd methods and potential application scenarios that could benefit from them (e.g. data clustering, semantic-aware indexing) while discussing current on-going challenges in the area. although extensively studied, the concept of lexical chains still has much to be explored. besides the fact that each idiom has its own identity, most of the presented work either relies solely on statistical approaches (e.g. tf-idf, bow) or focuses on one aspect of word relatedness. some research groups focus their efforts on exploring algorithms and tools to calculate distances between several entities, such as words, paragraphs, synonyms and lexical chains. a few rely on annotated text and/or machine learning techniques to extract semantic-like features from documents. others expand the set for each word, considering their immediate synonyms or hypernyms to improve corpus or query. the ones inspecting lexical chains, build them using the words individually, or often using some common/direct synonym. although these are interesting approaches, they are only focused on the word itself, leading to an alternative bow representation. they still do not explicitly consider the context of a given word in relation to its location or surroundings in the text. semantic and contextual aspects are difficult to track, but are important aspects of effective human communication. in the last eleven years, the interest in these topics and their contributions to traditional approaches have been increasing among distinct scientific communities. for example, @xcite examined the research conducted in the multimedia arena, consisting of 2,872 items (e.g. papers, journals, reports) in the last 11 years, and found an increasing number of publications exploring semantics and contextual aspects in different areas, pointing to a trend in these areas. our approach contributes to this topic by expanding the notion of wsd, considering all synsets of a given word, including the influence among them. furthermore, our chains are produced by using the most suitable synset for a word, which is a result of the evaluation of its contiguous neighbors (context). in addition, our lexical chains consider all desired hypernyms in wn, given a certain threshold, which can be adjusted to obtain higher (more general) or lower (less general) semantic representations. ## building extended lexical chains as stated by @xcite ) @xcite , there are multiple categories in which lexical chains can be classified. these concepts are explored in our approach through wn by using synsets and hypernyms. wn is a lexical database that provides a complex structure of how words and their meanings are related. the following is a small summary of the main terms necessary to understand our work using extensible lexical chains and wn @xcite ): a synset is a set of synonyms (one or more) for a given word, while hypernyms are sets of more general synsets. for example: pug and bulldog are each a kind of dog. a mammal is a generalization of dog, and so on. our model consists of exploring documents through their lexical structure. this will be provided by evaluating the semantic value of each word in a text @xcite . the main idea can be divided into four major tasks: (i) document extraction process, (ii) best synset disambiguation module, (iii) lexical synset chain extraction module and (iv) distributed semantic mapping. in (i), we select the documents to be processed and clean the data, eliminating noise, such as stopwords, special characters, punctuation, and html tags, among others. in this paper, the source of data was a set of webpages from wikipedia, so an enhanced stopwords' list had to be used. once the documents are preprocessed, we filter only those words that have a synset match in wn. if a word in the text has no match in wn it will not contribute to the formation of lexical chains, so we assume that it can be discarded. ## final considerations and future work in this paper, we explored how extracted semantic features can aid in document retrieval tasks. furthermore, we presented several contributions on how these features can be extracted to form more robust lexical chains. first, we explored the notion of wsd and how to represent words, considering the effect of their immediate neighbors in their meaning (bsd). second, a new methodology to transform variable length size semantic chains (flexlc) into fixed parametrized structures is proposed through the flex2fix algorithm. third we proposed an algorithm to derive fixlc directly from semantic representations. also, three variations of how to calculate the relative distance of those chains were explored. to establish a comparison with the proposed approaches, we compared them with traditional 3 data.