Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHow (Non-)Optimal is the Lexicon?
كيف (غير) الأمثل هو المعجم؟
417
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يعد تعيين المعاني المعجمية إلى WordForms ميزة رئيسية للغات الطبيعية. في حين أن ضغوط الاستخدام قد تقوم بتعيين كلمات قصيرة معاني متكررة (قانون اختصار Zipf)، فإن الحاجة إلى مفردات إنتاجية ومفتوحة، وقيود محلية على تسلسل الرموز، وعوامل أخرى مختلفة جميعها تشكل طمئتي لغات العالم. على الرغم من أهميتها في تشكيل الهيكل المعجمي، لم يتم تحديد المساهمات النسبية لهذه العوامل بالكامل. أخذ رؤية نظرية ترميز من المعجم والاستفادة من نموذج إحصائي عام جديد، نحدد الحدود العليا لضغوط المعجم تحت قيود مختلفة. فحص كوربورا من 7 لغات متنوعة من 7، نستخدم تلك الحدود العليا لتحديد فائنة المعجم واستكشاف التكاليف النسبية للقيود الرئيسية على الرموز الطبيعية. نجد أن التورفولوجيا (التركيبية) والرسومات الحربية يمكن أن يمثل بما فيه الكفاية لمعظم تعقيد الرموز الطبيعية --- كما تقاس طول التعليمات البرمجية.
The mapping of lexical meanings to wordforms is a major feature of natural languages. While usage pressures might assign short words to frequent meanings (Zipf's law of abbreviation), the need for a productive and open-ended vocabulary, local constraints on sequences of symbols, and various other factors all shape the lexicons of the world's languages. Despite their importance in shaping lexical structure, the relative contributions of these factors have not been fully quantified. Taking a coding-theoretic view of the lexicon and making use of a novel generative statistical model, we define upper bounds for the compressibility of the lexicon under various constraints. Examining corpora from 7 typologically diverse languages, we use those upper bounds to quantify the lexicon's optimality and to explore the relative costs of major constraints on natural codes. We find that (compositional) morphology and graphotactics can sufficiently account for most of the complexity of natural codes---as measured by code length.
References used
https://aclanthology.org/
rate research
Read More
Precise information of word boundary can alleviate the problem of lexical ambiguity to improve the performance of natural language processing (NLP) tasks. Thus, Chinese word segmentation (CWS) is a fundamental task in NLP. Due to the development of p
re-trained language models (PLM), pre-trained knowledge can help neural methods solve the main problems of the CWS in significant measure. Existing methods have already achieved high performance on several benchmarks (e.g., Bakeoff-2005). However, recent outstanding studies are limited by the small-scale annotated corpus. To further improve the performance of CWS methods based on fine-tuning the PLMs, we propose a novel neural framework, LBGCN, which incorporates a lexicon-based graph convolutional network into the Transformer encoder. Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010). Further experiments and analyses demonstrate that our proposed framework effectively models the lexicon to enhance the ability of basic neural frameworks and strengthens the robustness in the cross-domain scenario.
Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Most prior work on ML based lemmatization has focused on high resource languages, where data sets (word forms) are readily availabl
e. For languages which have no linguistic work available, especially on morphology or in languages where the computational realization of linguistic rules is complex and cumbersome, machine learning based lemmatizers are the way togo. In this paper, we devote our attention to lemmatisation for low resource, morphologically rich scheduled Indian languages using neural methods. Here, low resource means only a small number of word forms are available. We perform tests to analyse the variance in monolingual models' performance on varying the corpus size and contextual morphological tag data for training. We show that monolingual approaches with data augmentation can give competitive accuracy even in the low resource setting, which augurs well for NLP in low resource setting.
The paper presents experiments in neural machine translation with lexical constraints into a morphologically rich language. In particular and we introduce a method and based on constrained decoding and which handles the inflected forms of lexical ent
ries and does not require any modification to the training data or model architecture. To evaluate its effectiveness and we carry out experiments in two different scenarios: general and domain-specific. We compare our method with baseline translation and i.e. translation without lexical constraints and in terms of translation speed and translation quality. To evaluate how well the method handles the constraints and we propose new evaluation metrics which take into account the presence and placement and duplication and inflectional correctness of lexical terms in the output sentence.
Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social medi
a, which embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to any other language. The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.
Natural conversations are filled with disfluencies. This study investigates if and how BERT understands disfluency with three experiments: (1) a behavioural study using a downstream task, (2) an analysis of sentence embeddings and (3) an analysis of
the attention mechanism on disfluency. The behavioural study shows that without fine-tuning on disfluent data, BERT does not suffer significant performance loss when presented disfluent compared to fluent inputs (exp1). Analysis on sentence embeddings of disfluent and fluent sentence pairs reveals that the deeper the layer, the more similar their representation (exp2). This indicates that deep layers of BERT become relatively invariant to disfluency. We pinpoint attention as a potential mechanism that could explain this phenomenon (exp3). Overall, the study suggests that BERT has knowledge of disfluency structure. We emphasise the potential of using BERT to understand natural utterances without disfluency removal.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
