Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods
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paper_title: Bleu: A Method For Automatic Evaluation Of Machine Translation

paper_content:

Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.
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paper_title: Understanding The Thematic Structure Of The Qur'an: An Exploratory Multivariate Approach

paper_content:

In this paper, we develop a methodology for discovering the thematic structure of the Qur'an based on a fundamental idea in data mining and related disciplines: that, with respect to some collection of texts, the lexical frequency profiles of the individual texts are a good indicator of their conceptual content, and thus provide a reliable criterion for their classification relative to one another. This idea is applied to the discovery of thematic interrelationships among the suras (chapters) of the Qur'an by abstracting lexical frequency data from them and then applying hierarchical cluster analysis to that data. The results reported here indicate that the proposed methodology yields usable results in understanding the Qur'an on the basis of its lexical semantics.
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paper_title: Disambiguation by short contexts

paper_content:

This paper describes a technique that we believe can be of great help in many text-processing situations, and reports on an experiment recently conducted to test its validity and scope. As a background we shall present in the following sections some fundamental clarifications and remarks on our specific view of lemmatization and disambiguation. Our starting point is the double assertion that we believe would be shared by many workers in applied computational linguistics and large text-processing projects, to wit: that on the one hand lemmatization is one of the most important and crucial steps in many non-trivial text-processing cycles, but on the other hand, no operational, reasonably general, fully automatic and high-quality context-sensitive text-lemmatization system nowadays is easily accessible for any natural language. Given these two premises, the problem is how to introduce a partial element (at least) of machineaided work in the process of text-lemmatization, so as to avoid the extremely laborious and frustrating task of a word-per-word manual lemmatization of large corpora as was done in the early days of automatic text-processing projects. (For a thorough report on mechanical lemmatization programs, see ref. 4.) In this paper we focus on the analysis and experimental testing of one idea that fits naturally into this framework, namely that of disambiguation by short contexts. (The somewhat unexpected shift from "lemmatization" to "disambiguation" will be justified in the sections to come.) Based on
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paper_title: Experiments in automatic statistical thesaurus construction

paper_content:

A well constructed thesaurus has long been recognized as a valuable tool in the effective operation of an information retrieval system. This paper reports the results of experiments designed to determine the validity of an approach to the automatic construction of global thesauri (described originally by Crouch in [1] and [2] based on a clustering of the document collection. The authors validate the approach by showing that the use of thesauri generated by this method results in substantial improvements in retrieval effectiveness in four test collections. The term discrimination value theory, used in the thesaurus generation algorithm to determine a term's membership in a particular thesaurus class, is found not to be useful in distinguishing a “good” from an “indifferent” or “poor” thesaurus class). In conclusion, the authors suggest an alternate approach to automatic thesaurus construction which greatly simplifies the work of producing viable thesaurus classes. Experimental results show that the alternate approach described herein in some cases produces thesauri which are comparable in retrieval effectiveness to those produced by the first method at much lower cost.
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paper_title: Bleu: A Method For Automatic Evaluation Of Machine Translation

paper_content:

Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.
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paper_title: Concept based query expansion

paper_content:

Query expansion methods have been studied for a long time - with debatable success in many instances. In this paper we present a probabilistic query expansion model based on a similarity thesaurus which was constructed automatically. A similarity thesaurus reflects domain knowledge about the particular collection from which it is constructed. We address the two important issues with query expansion: the selection and the weighting of additional search terms. In contrast to earlier methods, our queries are expanded by adding those terms that are most similar to the concept of the query, rather than selecting terms that are similar to the query terms. Our experiments show that this kind of query expansion results in a notable improvement in the retrieval effectiveness when measured using both recall-precision and usefulness.
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paper_title: Using WordNet-Based Context Vectors To Estimate The Semantic Relatedness Of Concepts

paper_content:

In this paper, we introduce a WordNetbased measure of semantic relatedness by combining the structure and content of WordNet with co–occurrence information derived from raw text. We use the co–occurrence information along with the WordNet definitions to build gloss vectors corresponding to each concept in WordNet. Numeric scores of relatedness are assigned to a pair of concepts by measuring the cosine of the angle between their respective gloss vectors. We show that this measure compares favorably to other measures with respect to human judgments of semantic relatedness, and that it performs well when used in a word sense disambiguation algorithm that relies on semantic relatedness. This measure is flexible in that it can make comparisons between any two concepts without regard to their part of speech. In addition, it can be adapted to different domains, since any plain text corpus can be used to derive the co–occurrence information.
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paper_title: Word Sense Discrimination By Clustering Contexts In Vector And Similarity Spaces

paper_content:

This paper systematically compares unsupervised word sense discrimination techniques that cluster instances of a target word that occur in raw text using both vector and similarity spaces. The context of each instance is represented as a vector in a high dimensional feature space. Discrimination is achieved by clustering these context vectors directly in vector space and also by finding pairwise similarities among the vectors and then clustering in similarity space. We employ two different representations of the context in which a target word occurs. First order context vectors represent the context of each instance of a target word as a vector of features that occur in that context. Second order context vectors are an indirect representation of the context based on the average of vectors that represent the words that occur in the context. We evaluate the discriminated clusters by carrying out experiments using sense–tagged instances of 24 SENSEVAL2 words and the well known Line, Hard and Serve sense–tagged corpora.
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paper_title: Distinguishing Word Senses In Untagged Text

paper_content:

This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word to a known sense definition based solely on the values of automatically identifiable features in text. These methods and feature sets are found to be more successful in disambiguating nouns rather than adjectives or verbs. Overall, the most accurate of these procedures is McQuitty's similarity analysis in combination with a high dimensional feature set.
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paper_title: A web-based kernel function for measuring the similarity of short text snippets

paper_content:

Determining the similarity of short text snippets, such as search queries, works poorly with traditional document similarity measures (e.g., cosine), since there are often few, if any, terms in common between two short text snippets. We address this problem by introducing a novel method for measuring the similarity between short text snippets (even those without any overlapping terms) by leveraging web search results to provide greater context for the short texts. In this paper, we define such a similarity kernel function, mathematically analyze some of its properties, and provide examples of its efficacy. We also show the use of this kernel function in a large-scale system for suggesting related queries to search engine users.
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paper_title: Evaluation Of Utility Of LSA For Word Sense Discrimination

paper_content:

The goal of the on-going project described in this paper is evaluation of the utility of Latent Semantic Analysis (LSA) for unsupervised word sense discrimination. The hypothesis is that LSA can be used to compute context vectors for ambiguous words that can be clustered together --- with each cluster corresponding to a different sense of the word. In this paper we report first experimental result on tightness, separation and purity of sense-based clusters as a function of vector space dimensionality and using different distance metrics.
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paper_title: A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge

paper_content:

How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched.
---
paper_title: Word Sense Discrimination By Clustering Contexts In Vector And Similarity Spaces

paper_content:

This paper systematically compares unsupervised word sense discrimination techniques that cluster instances of a target word that occur in raw text using both vector and similarity spaces. The context of each instance is represented as a vector in a high dimensional feature space. Discrimination is achieved by clustering these context vectors directly in vector space and also by finding pairwise similarities among the vectors and then clustering in similarity space. We employ two different representations of the context in which a target word occurs. First order context vectors represent the context of each instance of a target word as a vector of features that occur in that context. Second order context vectors are an indirect representation of the context based on the average of vectors that represent the words that occur in the context. We evaluate the discriminated clusters by carrying out experiments using sense–tagged instances of 24 SENSEVAL2 words and the well known Line, Hard and Serve sense–tagged corpora.
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paper_title: Indexing by Latent Semantic Analysis

paper_content:

A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.
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paper_title: A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge

paper_content:

How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched.
---
paper_title: Using WordNet-Based Context Vectors To Estimate The Semantic Relatedness Of Concepts

paper_content:

In this paper, we introduce a WordNetbased measure of semantic relatedness by combining the structure and content of WordNet with co–occurrence information derived from raw text. We use the co–occurrence information along with the WordNet definitions to build gloss vectors corresponding to each concept in WordNet. Numeric scores of relatedness are assigned to a pair of concepts by measuring the cosine of the angle between their respective gloss vectors. We show that this measure compares favorably to other measures with respect to human judgments of semantic relatedness, and that it performs well when used in a word sense disambiguation algorithm that relies on semantic relatedness. This measure is flexible in that it can make comparisons between any two concepts without regard to their part of speech. In addition, it can be adapted to different domains, since any plain text corpus can be used to derive the co–occurrence information.
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Title: Computational Approaches to Measuring the Similarity of Short Contexts: A Review of Applications and Methods

Section 1: Introduction
Description 1: Overview of short written contexts, their importance, and why measuring their similarity poses a challenge.

Section 2: Similar Contexts
Description 2: Explanation of how similarity between concepts is defined and its application to short contexts.

Section 3: Types of Short Contexts
Description 3: Differentiation between headed and headless short contexts, including their characteristics and applications.

Section 4: Examples of Headed Contexts
Description 4: Illustration of how headed contexts can be used for word sense discrimination and name discrimination with specific examples.

Section 5: Examples of Headless Contexts
Description 5: Demonstration of how headless contexts are utilized by examining specific examples and discussing their broader applications.

Section 6: Applications
Description 6: Discussion on various problems that can be solved by identifying similar short contexts, including both headed and headless applications.

Section 7: Headless Applications
Description 7: Examination of different forms of headless contexts and their practical applications, such as automated grading and content filtering.

Section 8: Pair-wise Comparison of Headless Contexts to Reference Samples
Description 8: Overview of how headless contexts can be compared to reference samples in various applications like plagiarism detection and automated grading.

Section 9: Clustering N Headless Contexts
Description 9: Methods for clustering headless short contexts for organization and classification, including specific use cases.

Section 10: Headed / Target Word Applications
Description 10: Discussion on applications that focus on target words within contexts, including word sense disambiguation and name discrimination.

Section 11: Pair-wise Comparison of Headed Contexts to Reference Samples
Description 11: Analysis of how headed contexts are compared to reference samples to perform tasks like word sense disambiguation.

Section 12: Clustering N Headed Contexts
Description 12: Overview of clustering methods for headed short contexts to determine underlying meanings or senses of target words.

Section 13: Summary of Applications
Description 13: Summary of various applications of identifying similar short contexts and how different methods can approach these problems.

Section 14: First-order Similarity
Description 14: Explanation of first-order similarity methods based on word matching and their limitations.

Section 15: Second-order Similarity
Description 15: Introduction to second-order similarity methods using word vectors and their benefits over first-order methods.

Section 16: Word Vectors
Description 16: Description of how word vectors are constructed and used in second-order similarity methods.

Section 17: Micro View of Context
Description 17: In-depth look at the micro view of context involving local co-occurrences of words.

Section 18: Macro View of Context
Description 18: Examination of the macro view of context involving categorization based on the types of contexts where a word appears.

Section 19: Context as an Average of Word Vectors
Description 19: Methodology for representing short contexts as averages of word vectors and the advantages it provides for similarity measurement.

Section 20: Conclusion
Description 20: Summary of the discussed methods for measuring the similarity of short contexts and their practical implications.