Multiword Expressions Extraction Research Articles

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

Statistics based evaluation of English Multi-Word Expressions

The linguistic and statistical information extraction is an important aspect of text processing. The extraction of Multi Word Expression (MWEs) plays a key role in text processing as these are used to find correct meaning of a text phrase. MWEs are the lexical phrases consisting of two or more words conveying some different meaning together other than its constituent words. The linguistics in MWEs extraction is mainly related to the text information including the Part of Speech (POS) tags, grammar rules, related literature, and so on. It is important to extract the correct MWEs for a particular language as there exists variety and veracity in languages. The selection of MWEs are based on the statistical analysis of the MWEs extraction process. In the proposed work, the MWEs extraction is done for English dataset. Along with the existing statistical measures, i.e. Pointwise Mutual Information (PMI), Dice Coefficient (DC) and Modified Dice Coefficient (MDC), the additional measures, Lexical Fixedness (LF), Syntactic Fixedness (SF) and Relevance Measure (RM) are also been evaluated. The results are compared with the other existing approaches applied for English MWEs. The results shows that the proposed measures LF, SF and RM are more significant than existing measures to find the best statistics for the MWEs extraction process. The process model is generic in nature and not adhered to a particular language. It can also be applied for other languages by selecting POS tags for that particular language.

Advanced Clustering Technique to Handle Multi-word Expressions for Descriptive Documents

Expressive clustering comprises of naturally sorting out information occurrences into groups and creating a graphic outline for each group. The portrayal ought to advise a client about the substance regarding each group moving forward without any more assessment of the particular occasions, empowering a client to quickly filter for pertinent bunches. Choice of portrayals frequently depends on heuristic criteria. We model graphic grouping as an auto-encoder organize that predicts highlights from bunch assignments and predicts bunch assignments from a subset of highlights. We present an area free bunching based methodology for programmed extraction of multiword expressions (MWEs). The strategy consolidates factual data from a universally useful corpus and writings from Wikipedia articles. We fuse affiliation measures through elements of information focuses to bunch MWEs and after that process the positioning score for each MWE dependent on the nearest model doled out to a group. Assessment results, accomplished for two dialects, demonstrate that a mix of affiliation estimates gives an improvement in the positioning of MWEs contrasted and basic checks of co event frequencies and simply factual measures.

Hybrid method for automatic extraction of multiword expressions

A three phase hybrid method for automatic extraction of English multiword expressions (MWEs) has been proposed. The proposed method is based on linguistic patterns, association and context similarity between constituent words of the MWEs. First, the expressions are extract-ed in the form of N-grams from the raw text and then filtered using well defined linguistic patterns. Next, these expressions are again fil-tered using association score and context similarity score between their constituent words. Two association measures, Dice’s coefficient and PMI have been used for calculating the association score. The context similarity between words has been calculated using Latent Semantic Analysis (LSA) method. The problem of deciding the best value for the cut-off boundary thresholds in statistical methods is quite common. A two phase method of deciding the boundary threshold, using training dataset, has been proposed and employed in the current work. De-tailed performance analysis has been done on manually annotated dataset. The significant gain in performance has been observed for various types of multiword expressions.

LinguaKit: uma ferramenta multilingue para a análise linguística e a extração de informação

This paper presents LinguaKit, a multilingual suite of tools for analysis, extraction, annotation and linguistic correction. LinguaKit allows the user to perform different tasks such as lemmatization, PoS-tagging or syntactic parsing (among others), including applications for sentiment analysis (or opinion mining), extraction of multiword expressions or conceptual annotation and entity linking to DBpedia. Most part of the developed modules work in four linguistic varieties: Portuguese, Spanish, English, and Galician. The system is programmed in Perl, and it is freely available under a GPLv3 license.

Assigning clinical codes with data-driven concept representation on Dutch clinical free text

Clinical codes are used for public reporting purposes, are fundamental to determining public financing for hospitals, and form the basis for reimbursement claims to insurance providers. They are assigned to a patient stay to reflect the diagnosis and performed procedures during that stay. This paper aims to enrich algorithms for automated clinical coding by taking a data-driven approach and by using unsupervised and semi-supervised techniques for the extraction of multi-word expressions that convey a generalisable medical meaning (referred to as concepts). Several methods for extracting concepts from text are compared, two of which are constructed from a large unannotated corpus of clinical free text. A distributional semantic model (i.c. the word2vec skip-gram model) is used to generalize over concepts and retrieve relations between them. These methods are validated on three sets of patient stay data, in the disease areas of urology, cardiology, and gastroenterology. The datasets are in Dutch, which introduces a limitation on available concept definitions from expert-based ontologies (e.g. UMLS). The results show that when expert-based knowledge in ontologies is unavailable, concepts derived from raw clinical texts are a reliable alternative. Both concepts derived from raw clinical texts perform and concepts derived from expert-created dictionaries outperform a bag-of-words approach in clinical code assignment. Adding features based on tokens that appear in a semantically similar context has a positive influence for predicting diagnostic codes. Furthermore, the experiments indicate that a distributional semantics model can find relations between semantically related concepts in texts but also introduces erroneous and redundant relations, which can undermine clinical coding performance.

Computational Phraseology light: automatic translation of multiword expressions without translation resources

Abstract This paper describes the first phase of a project whose ultimate goal is the implementation of a practical tool to support the work of language learners and translators by automatically identifying multiword expressions (MWEs) and retrieving their translations for any pair of languages. The task of translating multiword expressions is viewed as a two-stage process. The first stage is the extraction of MWEs in each of the languages; the second stage is a matching procedure for the extracted MWEs in each language which proposes the translation equivalents. This project pursues the development of a knowledge-poor approach for any pair of languages which does not depend on translation resources such as dictionaries, translation memories or parallel corpora which can be time consuming to develop or difficult to acquire, being expensive or proprietary. In line with this philosophy, the methodology developed does not rely on any dictionaries or parallel corpora, nor does it use any (bilingual) grammars. The only information comes from comparable corpora, inexpensively compiled. The first proof-of-concept stage of this project covers English and Spanish and focuses on a particular subclass of MWEs: verb-noun expressions (collocations) such as take advantage, make sense, prestar atención and tener derecho. The choice of genre was determined by the fact that newswire is a widespread genre and available in different languages. An additional motivation was the fact that the methodology was developed as language independent with the objective of applying it to and testing it for different languages. The ACCURAT toolkit (Pinnis et al. 2012; Skadina et al. 2012; Su and Babych 2012a) was employed to compile automatically the comparable corpora and documents only above a specific threshold were considered for inclusion. More specifically, only pairs of English and Spanish documents with comparability score (cosine similarity) higher 0.45 were extracted.However, see section 6 which discusses experiments with different comparability scores. Statistical association measures were employed to quantify the strength of the relationship between two words and to propose that a combination of a verb and a noun above a specific threshold would be a (candidate for) multiword expression. This study focused on and compared four popular and established measures along with frequency: Log-likelihood ratio, T-Score, Log Dice and Salience. This project follows the distributional similarity premise which stipulates that translation equivalents share common words in their contexts and this applies also to multiword expressions. The Vector Space Model is traditionally used to represent words with their co-occurrences and to measure similarity. The vector representation for any word is constructed from the statistics of the occurrences of that word with other specific/context words in a corpus of texts. In this study, the word2vec method (Mikolov et al. 2013) was employed. Mikolov et al.'s method utilises patterns of word co-occurrences within a small window to predict similarities among words. Evaluation results are reported for both extracting MWEs and their automatic translation. A finding of the evaluation worth mentioning is that the size of the comparable corpora is more important for the performance of automatic translation of MWEs than the similarity between them as long as the comparable corpora used are of minimal similarity.

Time-sensitive Arabic multiword expressions extraction from social networks

In this paper, we present a comprehensive approach for extracting and relating Arabic multiword expressions (MWE) from Social Networks. 15 million tweets were collected and processed to form our da...

What do we get from extracting collocations?

This paper applies linguistic analysis to the results from the automatic extraction of multiword expressions in order to understand whether they are reliable from the theoretical point of view. The nature of the extracted units is discussed and illustrated with examples of Russian prepositions: first classified according to I. Mel’?uk’s theory (1995) and then re-analysed using the notion of constructions. The corpus-driven approach reveals the shortcomings in the prevalent way of describing multiword expressions in terms of strict classes, and the present paper can be thought of as providing a theoretical basis for the development of a new approach to their description.

Modeling the internal variability of multiword expressions through a pattern-based method

The issue of internal variability of multiword expressions (MWEs) is crucial towards their identification and extraction in running text. We present a corpus-supported and computational study on Italian MWEs, aimed at defining an automatic method for modeling internal variation, exploiting frequency and part-of-speech (POS) information. We do so by deriving an XML-encoded lexicon of MWEs based on a manually compiled dictionary, which is then projected onto a a large corpus. Since a search for fixed forms suffers from low recall, while an unconstrained flexible search for lemmas yields a loss in precision, we suggest a procedure aimed at maximizing precision in the identification of MWEs within a flexible search. Our method builds on the idea that internal variability can be modelled via the novel introduction of variation patterns , which work over POS patterns, and can be used as working tools for controlling precision. We also compare the performance of variation patterns to that of association measures, and explore the possibility of using variation patterns in MWE extraction in addition to identification. Finally, we suggest that corpus-derived, pattern-related information can be included in the original MWE lexicon by means of an enriched coding and the creation of an XML-based repository of patterns.

Extraction of multi-word expressions from small parallel corpora

AbstractWe present a general, novel methodology for extracting multi-word expressions (MWEs) of various types, along with their translations, from small, word-aligned parallel corpora. Unlike existing approaches, we focus on misalignments; these typically indicate expressions in the source language that are translated to the target in a non-compositional way. We introduce a simple algorithm that proposes MWE candidates based on such misalignments, relying on 1:1 alignments as anchors that delimit the search space. We use a large monolingual corpus to rank and filter these candidates. Evaluation of the quality of the extraction algorithm reveals significant improvements over naïve alignment-based methods. The extracted MWEs, with their translations, are used in the training of a statistical machine translation system, showing a small but significant improvement in its performance.

Комбинирование признаков для извлечения тематических цепочек в новостном кластере

Annotation. In this paper we consider a method for extraction of alternative names of a concept or a named entity mentioned in a news cluster. The method is based on the structural organization of news clusters and exploits comparison of various contexts of words. The word contexts are used as basis for multiword expression extraction and main entity detection. At the end of cluster processing we obtain groups of near-synonyms, in which the main synonym of a group is determined.

A hybrid framework to extract bilingual multiword expression from free text

Bilingual multiword expression extraction is always a significant problem in extracting meaning from free text. This involves analyzing large amounts of textual information. In this paper we propose a text mining approach to extract bilingual multiword expression. Both statistic and rule-based methods are employed into the system. There are two phases in the extraction process. In the first phase, lots of candidates are extracted from the corpus by statistic methods. The algorithm of multiple sequence alignment is sensitive to the flexible multiword. In the second phase, error-driven rules and patterns are extracted from corpus. For acquired high qualified instances, the manual work with active learning is also performed in sample selection. These trained rules are used to filter the candidates. Bilingual comparisons are used in a parallel corpus. Parts of bilingual syntactic patterns are obtained from the bilingual phrase dictionary. Some related experiments are designed for achieving the best performance because there are lots of parameters in this system. Experimental results showed our approach gains good performance.

Alignment-based extraction of multiword expressions

Due to idiosyncrasies in their syntax, semantics or frequency, Multiword Expressions (MWEs) have received special attention from the NLP community, as the methods and techniques developed for the treatment of simplex words are not necessarily suitable for them. This is certainly the case for the automatic acquisition of MWEs from corpora. A lot of effort has been directed to the task of automatically identifying them, with considerable success. In this paper, we propose an approach for the identification of MWEs in a multilingual context, as a by-product of a word alignment process, that not only deals with the identification of possible MWE candidates, but also associates some multiword expressions with semantics. The results obtained indicate the feasibility and low costs in terms of tools and resources demanded by this approach, which could, for example, facilitate and speed up lexicographic work.

Improving effectiveness of mutual information for substantival multiword expression extraction

One of the deficiencies of mutual information is its poor capacity to measure association of words with unsymmetrical co-occurrence, which has large amounts for multi-word expression in texts. Moreover, threshold setting, which is decisive for success of practical implementation of mutual information for multi-word extraction, brings about many parameters to be predefined manually in the process of extracting multiword expressions with different number of individual words. In this paper, we propose a new method as EMICO (Enhanced Mutual Information and Collocation Optimization) to extract substantival multiword expression from text. Specifically, enhanced mutual information is proposed to measure the association of words and collocation optimization is proposed to automatically determine the number of individual words contained in a multiword expression when the multiword expression occurs in a candidate set. Our experiments showed that EMICO significantly improves the performance of substantival multiword expression extraction in comparison with a classic extraction method based on mutual information.

Comparing and combining a semantic tagger and a statistical tool for MWE extraction

Automatic extraction of multiword expressions (MWEs) presents a tough challenge for the NLP community and corpus linguistics. Indeed, although numerous knowledge-based symbolic approaches and statistically driven algorithms have been proposed, efficient MWE extraction still remains an unsolved issue. In this paper, we evaluate the Lancaster UCREL Semantic Analysis System (henceforth USAS (Rayson, P., Archer, D., Piao, S., McEnery, T., 2004. The UCREL semantic analysis system. In: Proceedings of the LREC-04 Workshop, Beyond Named Entity Recognition Semantic labelling for NLP tasks, Lisbon, Portugal. pp. 7–12)) for MWE extraction, and explore the possibility of improving USAS by incorporating a statistical algorithm. Developed at Lancaster University, the USAS system automatically annotates English corpora with semantic category information. Employing a large-scale semantically classified multi-word expression template database, the system is also capable of detecting many multiword expressions, as well as assigning semantic field information to the MWEs extracted. Whilst USAS therefore offers a unique tool for MWE extraction, allowing us to both extract and semantically classify MWEs, it can sometimes suffer from low recall. Consequently, we have been comparing USAS, which employs a symbolic approach, to a statistical tool, which is based on collocational information, in order to determine the pros and cons of these different tools, and more importantly, to examine the possibility of improving MWE extraction by combining them. As we report in this paper, we have found a highly complementary relation between the different tools: USAS missed many domain-specific MWEs (law/court terms in this case), and the statistical tool missed many commonly used MWEs that occur in low frequencies (lower than three in this case). Due to their complementary relation, we are proposing that MWE coverage can be significantly increased by combining a lexicon-based symbolic approach and a collocation-based statistical approach.