This paper is devoted to the development of a methodology for evaluating the semantic similarity of any texts in different languages is developed. The study is based on the hypothesis that the proximity of vector representations of terms in semantic space can be interpreted as a semantic similarity in the cross-lingual environment. Each text will be associated with a vector in a single multilingual semantic vector space. The measure of the semantic similarity of texts will be determined by the measure of the proximity of the corresponding vectors. We propose a quantitative indicator called Index of Semantic Textual Similarity (ISTS) that measures the degree of semantic similarity of multilingual texts on the basis of identified cross-lingual semantic implicit links. The setting of parameters is based on the correlation with the presence of a formal reference between documents. The measure of semantic similarity expresses the existence of two common terms, phrases or word combinations. Optimal parameters of the algorithm for identifying implicit links are selected on the thematic collection by maximizing the correlation of explicit and implicit connections. The developed algorithm can facilitate the search for close documents in the analysis of multilingual patent documentation.
As cross-language information retrieval gets more
attention, tools to measure cross-language semantic
similarity between documents become necessary. An
accurate assessment of the actual similarity between
documents is fundamental for many automatic text
analysis applications, such as thesaurus generation [
Text mining and knowledge management technologies play a key role in many areas, including critical infrastructures. Information search, document classification, business analytics, forecasting technologies, etc. are currently the most important activities.
Patent search, including monitoring competitors, checking the novelty of an invention, or searching for technical solutions in other fields of application, requires a lot of effort.
Comparing documents in different languages is challenging for natural language processing applications, and especially in machine translation applications.
Cross-language matching of documents is carried out in a patent search to protect an invention in more than one country or region. A separate patent must be filed with several patent offices in different languages. Before applying for a patent, applicants conduct a preliminary search for patents or documents revealing intellectual property similar to the filed invention. In such a process, a set of patents is requested in one language, using the source document in another language as a request.
To compare the received documents, it is necessary to
use cross-language similarity assessment functions. This
task can be formulated as discarding text pairs that are not
semantically equivalent [
Natural language processing methods for text analysis and data mining are used in the analysis of many types of technical documentation. Functional analysis methods are based on extracting interactions between the entities described in the document.
Linguistic analysis tools permit to identify key elements of a document by combining morphological, syntactic, and semantic analysis. Application of methods of linguistic analysis to patent documents allows for accelerated analysis and comparison of patents.
The purpose of the analysis of technical documentation is to discover possible ambiguities or incompleteness on the one hand, and understanding the requirements in the direction of possible formalization on the other.
The main problem here is that keyword searches do not
take into account synonyms or more abstract terms
associated with given query words. This means that if a
synonym is used for an important term in a patent
application, for example, a wire instead of a cable, a
keyword search may not reveal this relationship if an
alternative term was not explicitly included in the search
query. This is relevant since patent texts often use abstract
and general terms to describe the invention in order to
maximize protection [
If we consider the Internet as a multilingual database, a typical problem when searching for information is the search for relevant documents in the collection of documents by some key terms, or by the example of the corresponding document. Assessing the semantic similarity between words (phrases) is critical to assessing whether a document meets user needs. Many information retrieval systems, such as online library catalog systems, web search engines, deal with multilingual documents and must have tools to measure cross-language semantic similarity.
In recent decades, many studies have been carried out aimed at improving the effectiveness of measures of semantic similarity of words. However, studies of semantic similarity mainly focus on English. This is partly due to the limited availability of similarity criteria for words in languages other than English. Since the development of multilingual methods is necessary, there is an urgent need to find a reliable basis for assessing multilingual and interlingual semantic similarity.
Despite the fact that in many areas a multilingual
measurement of semantic semantic similarity is required,
most algorithms measure semantic similarity between
words of the same language. Cross-language similarity
was first described in 2009 [
Interlanguage applications are based on data mining methods, such as text clustering, which includes extracting words or phrases from documents as functions, representing documents as feature vectors, and then grouping documents into clusters based on similarity of feature vectors. In a multilingual document collection, recoverable functions will refer to multilingual words. Therefore, it is important to measure the similarity between the words of not only one language, but also of different languages.
According to the concept of the information data space
[
The problem of plagiarism in a monolingual context is
well developed [
The technique includes the following steps: 1) pre-processing of texts by replacing their terms with
synset codes; 2) construction of quotation vectors by identifying common rare phrases (long quotes) in various documents using the relevant phrases method; 3) thematic analysis of processed texts and building a set of available topics and corresponding thematic document vectors using the LDA method with the possibility of further clustering documents on topics / ideas into “baskets”/clusters; 4) the construction for each document of an extended vector describing the presence of long citations, the statistics of the synsets included in it and their thematic composition, i.e. the document vector is the concatenation of the citation vector, thematic vector and synset statistics vector; 5) calculation of the similarity index between articles/documents (Semantic Text Similarity Index, ISTS) by the cosine measure of the corresponding article vectors; 6) calculation of the correlation between the formal connectedness of articles and their similarity index, taking into account the minimum and maximum thresholds of the ISTS; 7) the choice of values of various calculation parameters (ISTS thresholds) based on the maximum correlation.
The calculation method is selected according to the maximum correlation of ISTS with formal links.
In the basis of the algorithm for vector transformation of terms used recurrent neural networks (RNN - Recurrent neural network) - Fig. 1.
Fig. 1. Graphs of the number of points for calculating the correlations of the current and future years for the indicators IFTm (upper) and IFT, depending on the number of articles with the word in the last 3 years
RNN is used for tasks where there is a sequence of words and phrases. Formally, at each step (after each new processed word), RNN considers for each word in the corpus the probability of which word will be next. In this work, LSTM neurons, which are a special case of RNN, were used. Moreover, bi-directional recurrent biLSTM network (Bidirectional recurrent neural networks) was used. biLSTM is a combination of two LSTM networks in which at the same time one network builds a language model from the beginning of the sentence, and the second from the end.
We used the simplest sequential model, consisting of two layers. For the software implementation of the proposed architecture in Python, the jupyter notebook development environment was used. A linear layer was attached to the biLSTM layer to solve the classification problem (Fig. 2).
At the input of the neural network, vector representations of words (embending) were applied.
Word2Vec was used to convert each word from the title of the article to a number vector. In the experiments, 300 dimension vectors were used (Word2Vec from the gensim library allows changing the embedding dimension).
In our experiments, we consider the DBLP citation network, a collection of articles on artificial intelligence compiled by aminer.org. In this study, we intentionally relied only on the title of the publication and its links.
During the experiments, various models of the neural network were tested. Experiments were conducted with a change in the number of neurons in the biLSTM layer (4, 8, 16, 32, 64, 128) and the number of neurons in the linear layer (from 0 to 10). The best model was able to give an accuracy of 0.6131 according to the ROC AUC metric.
The time for calculating the forecast and evaluating its accuracy was about 1 hour.
To combine articles with similar topics into clusters, we used generally accepted approaches to machine word processing (NLP), clustering articles using the Latent Dirichlet Allocation (LDA) method, and visualizing the results obtained with Python libraries. After extracting the data, preprocessing it, extracting tokens, stamping and deleting stop words, we used the Latent Dirichlet Allocation (LDA) algorithm - Fig. 2.
LDA is a hierarchical Bayesian model that consists of two levels: at the first level, a mixture whose components correspond to “themes”; at the second level, a multinomial variable with an a priori Dirichlet distribution that defines the “distribution of topics” in the document.
The principle of the model: 1) select the document length N 2) vector is selected θ ~ (α) - the vector of the “degree of
expression” of each topic in this document; 3) for each of N words w: choose a theme zn by distribution Mult(θ); choose a word wn ~ p(wn|zn, β) with probabilities given in β.
For simplicity, we fix the number of topics k and assume that β is just a set of parameters βi,j = p(wj=1|zi=1), which need to be evaluated, and we won’t worry about the distribution on N. The joint distribution then looks like this:
Unlike conventional clustering with an a priori Dirichlet distribution, we do not select a cluster here once, and then we look for words from this cluster, but for each word we first select a topic from the distribution θ, and only then we relate this word to this topic.
At the output after training the LDA model, themed vectors θ are obtained, showing how topics are distributed in each document, and distributions β, which show which words are more likely in certain topics. In our case, we got 8 pronounced clusters corresponding to the following directions: 1) computing systems and algorithms in them; 2) bioinformatics and data processing methods in it; 3) signal processing; 4) optimization methods and algorithms based on them; 5) problems related to theoretical informatics and
computational complexity; 6) neural and computing networks; 7) issues regarding natural language processing (NLP)
and programming languages; 8) robotics, and self-learning systems (Reinforcement
Learning).
After the previous step, n-dimensional thematic vectors of articles are obtained. To compress the results into a two-dimensional vector space, the t-SNE machine learning algorithm was used. To visualize the clusters, we used an interface written in JavaScrIFT (Fig. 3).
The previous approach was based on a comparison of vectors at the megalemma level in a cosine measure, which determined the semantic similarity of the texts. As a development of this approach, based on the assumption that while maintaining the semantic similarity of phrases, ideas in them can be expressed in different words, we use the Impact Factor of the Term (IFT) to assess the similarity of documents.
To compare articles expressing new ideas, we use the hypothesis that new ideas are often expressed in terms of a high impact factor IFT. IFT is determined by the average number of links to articles with this term, the higher the IFT, the higher the citation trend and the number of formal links. If a couple of articles have a general term with a high IFT, the probability of a formal link between them will be high.
Using multilingual synsets built for high IFT terms (IFT terms), you can evaluate the similarity of articles in any language. If there is a semantic similarity, estimated by a cosine measure, it can be assumed that articles with this term will be quoted with some probability.
If previously the similarity of the vectors of megalemma determined the similarity of texts, now we use extended vectors based on common rare phrases, megalemmas and multilingual IFT synsets, as well as the results of thematic analysis. The similarity of extended vectors more accurately reflects the similarity of texts, since it takes into account not only semantic, but also thematic similarity.
Fig. 3. Cluster states in 1993. 1) computing systems and algorithms in them (pink); 2) bioinformatics and data processing methods in it (purple); 3) signal processing (brown); 4) optimization methods and algorithms based on them (green); 5) problems related to theoretical informatics and computational complexity (orange); 6) neural and computing networks (red); 7) issues regarding natural language processing (NLP) and programming languages (blue); 8) robotics, and self-learning systems (Reinforcement Learning) (dark orange); yellow - a “garbage” cluster with articles in German
Our study is based on a model for representing ideas in the form of many terms and similar phrases in a multilingual semantic field, on the hypothesis that the proximity of vector representations of terms in a multilingual vector semantic space can be interpreted as semantic similarity in an interlanguage environment. We propose a method of formalizing ideas by using terms with high IFT and megalemma, which allows you to recognize an idea expressed in different words. References, both formal (bibliographic) and contextual (implicit, expressed by matching IFT terms), are an expression of the connection between ideas.
High IFT terms are significant terms (or ideologically significant). If the texts on the IFT synsets have the same vector, then this means the presence of common ideas in these texts and a significant similarity related to citation.
The similarity in vectors of megalemmas also correlates with formal links (as our previous experiments showed), but to a much lesser extent. It is shown that megalemma has a very low impact factor.
It should be noted that the similarity in vectors of megalemmas is more applicable to texts with common vocabulary, in this case, the degree of coincidence of their thematic composition as a set of popular words is calculated. The approach to calculating the similarity of IFT / megalemma vectors is focused on comparing the similarity of scientific texts with specific terminology, despite the fact that ideas can have different lexical expressions. Therefore, in the second case, it becomes possible to more accurately assess the similarity from the point of view of ideological similarity, since terms with a high IFT are significant terms denoting ideas.
Three types of semantic similarity can be considered (based on implicit references): 1) similarity of the thematic composition of popular / common words (word frequency from 10 thousand or more); 2) the presence of common significant IFT terms denoting specific ideas (frequency 51000); 3) the presence of common rare phrases (long quotation) (frequency 2-100). These types differ in the frequency of matching terms / phrases. The highest frequency is typical for popular terms and megalemmas, the lowest is for common rare phrases. The proposed similarity assessment algorithm takes into account all these types of similarities, giving appropriate weights.
Thus, when identifying similarities and implicit references, the entire frequency range of terms and phrases is used.
So, we build extended vectors from megalemmas and multilingual IFT synsets, and these can be weighted vectors whose elements have weights. The larger the impact factor, the higher the likelihood of a formal link and the higher the weight of the vector element. The cosine measure allows you to work with weighted vectors, in which elements take large real values. Since our task is to search for semantic similarity of articles correlating with the presence of formal links, then increasing the weights of IFT synsets in extended vectors improves the quality of the proposed algorithm.
Therefore, the algorithm for calculating ISTS is based on assessing the similarity of vectors, expanded by adding multilingual IFT synsets and weights, according to a cosine measure, in order to determine the similarity of texts. This takes into account the presence of formal links between texts containing matching IFT terms. The method may contain options that are determined/selected by the optimization method according to the maximum correlation of ISTS with formal links.
The first version of the methodology for calculating the multilingual Index of Ideological Influence (III) as the number of similar subsequent / future articles / documents has been developed.
We consider similar subsequent articles to be articles
that will cite this document, i.e. those articles are similar
that are linked by formal links. Thus, the III is looking for
trending articles containing trending IFT terms. We can
calculate the second-level III, since one idea gives rise to
another, then you can search for articles similar to the
articles found in the first stage (indirect similarity /
similarity). The mutual influence of articles is calculated
using the PageRank algorithm [
IFT terms in scientific articles have an expiration date.
The value of IFT is higher in the first years (3-4 years), and then it decreases (Fig. 4).
Fig. 4. Graphs of the average values of the IFT term, depending on the number of articles with these terms and the speed of the trend. 1 - 0 years, 2 - 1 year, 3 - 2 years, 4 - 3 years, 5 - 4 years, 6 - 5 years
Over time, some important terms are replaced by others. If in the vector for the term, in addition to the IFT, another year is introduced when the term was of high importance, then you can also obtain some information about the age of the article by the vector, which will allow you to find general ideas of a certain age when comparing the articles. This provides information on the dynamics of the development of ideas. For example, the term NEURAL NETWORKS has a long history, and in different years, various derivatives of this term were significant IFT terms, for example, FUZZY NEURAL or RECURRENT neural networks.
Over time, some important terms are replaced by others. If in the vector for the term, in addition to the IFT, another year is introduced when the term was of high importance, then you can also obtain some information about the age of the article by the vector, which will allow you to find general ideas of a certain age when comparing the articles. This provides information on the dynamics of the development of ideas. For example, the term NEURAL NETWORKS has a long history, and in different years, various derivatives of this term were significant IFT terms, for example, FUZZY NEURAL or RECURRENT neural networks.
So, the methodology for calculating the III contains the following steps: 1) search in the article for significant IFT terms; 2) compiling multilingual IFT synsets for these IFT
terms;
3) on the basis of IFT-synsets, the definition of the
forecast (regression analysis according to previous
values of IFT and trend parameters);
4) refinement of the forecast using the PageRank
algorithm [
In this case, implicit links between texts/articles are determined using the methodology for calculating the index of semantic text similarity (ISTS).
As a result, we see the following pattern: the higher the forecast of the IFT, the higher the III of the document. The predictive value of the IFT is the same as the text, term, or idea. If there are several IFT terms in the text, then you can make a prediction according to the most significant/high IFT, or according to statistics that take into account the synergy of IFT terms when found together. An updated forecast of III/IFT is carried out using regression analysis using a number of indicators for the current year (IFT, IFTm, external links) and similar indicators of previous years.
The Multilingual Index of Ideological Influence (III) corresponds to the number of subsequent/future articles/documents citing the source document that are similar to the source document. We plan to consider a number of index modifications taking into account the cascade of citation (first and other levels) and the temporal dynamics of the development of ideas. It is planned to develop an algorithm for the updated forecast of III/IFT using a number of indicators of the current year (IFT, IFTm, external links) and similar indicators of previous years.
The reported study was funded by RFBR according to the research projects № 18-07-00909, 19-07-00857 and 20-04-60185.