Multiword expression processing: A survey

Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives

Statistical dependency parsing of Turkish

This paper presents results from the first statistical dependency parser for Turkish. Turkish is a free-constituent order language with complex agglutinative inflectional and derivational morphology and presents interesting challenges for statistical parsing, as in general, dependency relations are between “portions” of words called inflectional groups. We have explored statistical models that use different representational units for parsing. We have used the Turkish Dependency Treebank to train and test our parser but have limited this initial exploration to that subset of the treebank sentences with only left-to-right non-crossing dependency links. Our results indicate that the best accuracy in terms of the dependency relations between inflectional groups is obtained when we use inflectional groups as units in parsing, and when contexts around the dependent are employed

Dependency parsing of Turkish

The suitability of different parsing methods for different languages is an important topic in syntactic parsing. Especially lesser-studied languages, typologically different from the languages for which methods have originally been developed, poses interesting challenges in this respect. This article presents an investigation of data-driven dependency parsing of Turkish, an agglutinative free constituent order language that can be seen as the representative of a wider class of languages of similar type. Our investigations show that morphological structure plays an essential role in finding syntactic relations in such a language. In particular, we show that employing sublexical representations called inflectional groups, rather than word forms, as the basic parsing units improves parsing accuracy. We compare two different parsing methods, one based on a probabilistic model with beam search, the other based on discriminative classifiers and a deterministic parsing strategy, and show that the usefulness of sublexical units holds regardless of parsing method.We examine the impact of morphological and lexical information in detail and show that, properly used, this kind of information can improve parsing accuracy substantially. Applying the techniques presented in this article, we achieve the highest reported accuracy for parsing the Turkish Treebank

The incremental use of morphological information and lexicalization in data-driven dependency parsing

Typological diversity among the natural languages of the world poses interesting challenges for the models and algorithms used in syntactic parsing. In this paper, we apply a data-driven dependency parser to Turkish, a language characterized by rich morphology and flexible constituent order, and study the effect of employing varying amounts of morpholexical information on parsing performance. The investigations show that accuracy can be improved by using representations based on inflectional groups rather than word forms, confirming earlier studies. In addition, lexicalization and the use of rich morphological features are found to have a positive effect. By combining all these techniques, we obtain the highest reported accuracy for parsing the Turkish Treebank

Türkçe cümlelerin kural tabanlı bağlılık analizi

Bu makalede, Türkçe cümlelerin kural tabanlı bağlılık analizi yöntemi ile ayrıştırılmaları sonucunda elde edilen başarım sunulmaktadır. Çalışma, test verisi olarak kullanılan ODTÜ-Sabancı Ağaç Yapılı Derlemi'nin bütünü üzerindeki ilk kural tabanlı sonuçları içermektedir. Uygulanan ayrıştırma algoritması ve kural yapıları detaylı olarak verilmiştir. Sonuçlar Türkçe'nin Bağlılık Analizi konusunda yapılacak çalışmalara temel olma niteliğindedir

IMST: A Revisited Turkish Dependency Treebank

In this paper, we present a critical analysis of the dependency annotation framework used in the METU-Sabancı Treebank (MST), and propose new annotation schemes that would alleviate the issues we have identified. Later, we describe our attempt at reannotating the treebank from the ground up using the proposed schemes, and then compare the consistencies of the two versions via cross validation using a dependency parser. According to our experiments, the reannotated version of the original treebank, which we call the ITU-METU-Sabancı Treebank (IMST), demonstrates a labeled attachment score of 75.3% and an unlabeled attachment score of 83.7%, surpassing the corresponding scores of 65.9% and 76.0% for MST by a very large margin.Peer reviewe

Citation Recommendation on Scholarly Legal Articles

Citation recommendation is the task of finding appropriate citations based on a given piece of text. The proposed datasets for this task consist mainly of several scientific fields, lacking some core ones, such as law. Furthermore, citation recommendation is used within the legal domain to identify supporting arguments, utilizing non-scholarly legal articles. In order to alleviate the limitations of existing studies, we gather the first scholarly legal dataset for the task of citation recommendation. Also, we conduct experiments with state-of-the-art models and compare their performance on this dataset. The study suggests that, while BM25 is a strong benchmark for the legal citation recommendation task, the most effective method involves implementing a two-step process that entails pre-fetching with BM25+, followed by re-ranking with SciNCL, which enhances the performance of the baseline from 0.26 to 0.30 MAP@10. Moreover, fine-tuning leads to considerable performance increases in pre-trained models, which shows the importance of including legal articles in the training data of these models.Comment: Seventeenth International Workshop on Juris-informatics (JURISIN 2023

Probabilistic dependency parsing of Turkish

Bu çalışma, Türkçe için geliştirilmiş ilk istatistiksel bağlılık ayrıştırıcısının sonuçlarını sunmaktadır. Türkçe, tümce içi öğe dizilişleri serbest, karmaşık bir çekimsel ve türetimsel biçimbirime sahip olan bitişken bir dildir ve bu özellikleri ile istatistiksel ayrıştırma konusunda ilginç sorunlar ortaya koymaktadır. Türkçe’de, bağlılık ilişkileri “çekim kümesi” adı verilen sözcük parçacıkları arasında kurulmaktadır.  Bu bağlılıkların bulunması amacı ile Türkçe’nin karmaşık yapısının ayrıştırma sırasında nasıl modelleneceğinin irdelenmesi gerekmektedir. Bu çalışmada, ayrıştırma için farklı gösterim birimleri kullanan olasılık tabanlı modeller incelenmiştir. Başlangıç olarak biri kural tabanlı bir ayrıştırıcı olmak üzere üç dayanak model geliştirilmiştir. Gerçekleştirilen üç olasılık tabanlı modelin, dayanak modellere ve birbirlerine oranla başarımları değerlendirilmiştir. Ayrıştırıcının eğitimi ve sınaması için Odtü Sabancı Türkçe ağaç yapılı derlemi kullanılmıştır. Çalışma ayrıca bu derlem üzerinde sınanmış ve sonuçlaı raporlanmış ilk çalışmadır. Bu ilk incelemede, derlemin sadece sağa bağımlı (iye sözcüklerin uydu sözcüklerin sağ taraflarında yer aldığı) türde ve kesişmeyen  bağlılıklar içeren bir alt kümesini ayrıştırmaya odaklanılmıştır. Eldeki derlemin boyutu nedeni ile görünüm bilgisi (sözcüğün tümünün veya gövdesinin ayrıştırma birimi gösterimlerinde bir özellik olarak kullanılması) kullanmayan ve sadece birimler arası etiketsiz bağlılıkları bulmaya yönelik incelemeler yapılmıştır. Sonuçlarımız, çekim kümeleri arasındaki doğru bağlıkların bulunma başarımı gözönüne alındığında, ayrıştırma birimi olarak çekim kümelerinin kullanıldığı ve bağlam bilgisinden yararlanan modelin en yüksek başarımı sağladığını göstermektedir.  Anahtar Kelimeler: Bağlılık ayrıştırması, doğal dil işleme, ayrıştırma, sentaks analizi.This paper presents results from the first statistical dependency parser for Turkish. Turkish is a free-constituent order language with complex agglutinative inflectional and derivational morphology and presents interesting challenges for statistical parsing, as in general, dependency relations are between "portions" of words called inflectional groups. We have explored statistical models that use different representational units for parsing. We have used the Turkish Dependency Treebank to train and test our parser but have limited this initial exploration to that subset of the treebank sentences with only left-to-right non-crossing dependency links. Our results indicate that the best accuracy in terms of the dependency relations between inflectional groups is obtained when we use inflectional groups as units in parsing, and when contexts around the dependent are employed. Turkish shows very different characteristics from the well-studied languages in parsing literature. Many of these characteristics are common for all agglutinative languages such as Basque, Estonian, Finnish, Hungarian, Japanese and Korean. It is a flexible constituent order language. Even though in written texts, the constituent order of sentences generally conforms to the SOV or OSV structures, the constituents may freely change their position depending on the requirements of the discourse context. From the point of view of dependency structure, Turkish is predominantly (but not exclusively) head final. Furthermore, Turkish morphotactics is quite complicated: a given word form may involve multiple derivations and the number of word forms one can generate from a nominal or verbal root is theoretically infinite. Derivations in Turkish are very productive, and the syntactic relations that a word is involved in as a dependent or head element, are determined by the inflectional properties of the one or more (possibly intermediate) derived forms. In this work, we assume that a Turkish word is represented as a sequence of inflectional groups (IGs hereafter), separated by ?DBs, denoting derivation boundaries. A sentence would then be represented as a sequence of the IGs making up the words. When a word is considered as a sequence of IGs, linguistically, the last IG of a word determines its role as a dependent, so, syntactic relation links only emanate from the last IG of a (dependent) word, and land on one of the IGs of a (head) word on the right (with minor exceptions). And again with minor exceptions, the dependency links between the IGs, when drawn above the IG sequence, do not cross. We implemented three baseline parsers: 1. The first baseline parser links a word-final IG to the first IG of the next word on the right.2. The second baseline parser links a word-final IG to the last IG of the next word on the right. 3. The third baseline parser is a deterministic rule-based parser that links each word-final IG to an IG on the right based on the approach of Nivre (2003). The parser uses 23 unlexicalized linking rules and a heuristic that links any non-punctuation word not linked by the parser to the last IG of the last word as a dependent. In addition to these, we implemented three probabilistic models:1. 'Unlexicalized' Word-based Model, where the words are represented as the concatenation of their IGs and are used as the parsing unit during the parsing. 2. IG-based Model, where each word is splitted into its IGs and then the IGs are used as the smallest parsing unit. 3. IG-based Model with Word-final IG Contexts, where the IGs are again used as the parsing unit. This model differs from the previous one in the way it uses the contextual units and calculates the distances between units. Our results indicate that all of our models perform better than the three baseline parsers, even when no contexts around the dependent and head units are used. We get our best results with Model 3, where IGs are used as units for parsing and contexts are comprised of word final IGs. The highest accuracy in terms of percent of correctly extracted IG-to-IG relations excluding punctuations (73.5%) was obtained when one word is used as context on both sides of the the dependent. We also noted that using a smaller treebank to train our models did not result in a significant reduction in our accuracy indicating that the unlexicalized models are quite effective, but this also may hint that a larger treebank with unlexicalized modeling may not be useful for improving link accuracy.  Keywords: Dependency parsing, natural language processing, parsing, syntax analysis.

Universal dependencies for Turkish

The Universal Dependencies (UD) project was conceived after the substantial recent interest in unifying annotation schemes across languages. With its own annotation principles and abstract inventory for parts of speech, morphosyntactic features and dependency relations, UD aims to facilitate multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. This paper presents the Turkish IMST-UD Treebank, the first Turkish treebank to be in a UD release. The IMST-UD Treebank was automatically converted from the IMST Treebank, which was also recently released. We describe this conversion procedure in detail, complete with mapping tables. We also present our evaluation of the parsing performances of both versions of the IMST Treebank. Our findings suggest that the UD framework is at least as viable for Turkish as the original annotation framework of the IMST Treebank.Peer reviewe

SemEval-2016 task 5 : aspect based sentiment analysis

International audienceThis paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams