AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With (original) (raw)
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arXiv (Cornell University), 2022
We present a new pre-trained language model (PLM) for modern Hebrew, termed AlephBERT-Gimmel, which employs a much larger vocabulary (128K items) than standard Hebrew PLMs before. We perform a contrastive analysis of this model against all previous Hebrew PLMs (mBERT, heBERT, AlephBERT) and assess the effects of larger vocabularies on task performance. Our experiments show that larger vocabularies lead to fewer splits, and that reducing splits is better for model performance, across different tasks. All in all this new model achieves new SOTA on all available Hebrew benchmarks, including Morphological Segmentation, POS Tagging, Full Morphological Analysis, NER, and Sentiment Analysis. Subsequently we advocate for PLMs that are larger not only in terms of number of layers or training data, but also in terms of their vocabulary. 1
Hebrew Named Entity Recognition
MONEY
This paper presents a named entity recognition (NER) system for the Hebrew language. The Hebrew language has high morphological ambiguity, which makes automatic processing difficult. The first step in our work was to define the tagging task for the Hebrew language.
Choosing an optimal architecture for segmentation and POS-tagging of Modern Hebrew
2005
A major architectural decision in designing a disambiguation model for segmentation and Part-of-Speech (POS) tagging in Semitic languages concerns the choice of the input-output terminal symbols over which the probability distributions are defined. In this paper we develop a segmenter and a tagger for Hebrew based on Hidden Markov Models (HMMs). We start out from a morphological analyzer and a very small morphologically annotated corpus. We show that a model whose terminal symbols are word segments (=morphemes), is advantageous over a word-level model for the task of POS tagging. However, for segmentation alone, the morpheme-level model has no significant advantage over the word-level model. Error analysis shows that both models are not adequate for resolving a common type of segmentation ambiguity in Hebrew-whether or not a word in a written text is prefixed by a definiteness marker. Hence, we propose a morphemelevel model where the definiteness morpheme is treated as a possible feature of morpheme terminals. This model exhibits the best overall performance, both in POS tagging and in segmentation. Despite the small size of the annotated corpus available for Hebrew, the results achieved using our best model are on par with recent results on Modern Standard Arabic.
HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark
2022
Current benchmarks for Hebrew Natural Language Processing (NLP) focus mainly on morpho-syntactic tasks, neglecting the semantic dimension of language understanding. To bridge this gap, we set out to deliver a Hebrew Machine Reading Comprehension (MRC) dataset, where MRC is to be realized as extractive Question Answering. The morphologically-rich nature of Hebrew poses a challenge to this endeavor: the indeterminacy and non-transparency of span boundaries in morphologically complex forms lead to annotation inconsistencies, disagreements, and flaws in standard evaluation metrics. To remedy this, we devise a novel set of guidelines, a controlled crowdsourcing protocol, and revised evaluation metrics, that are suitable for the morphologically rich nature of the language. Our resulting benchmark, HeQ (Hebrew QA), features 30,147 diverse question-answer pairs derived from both Hebrew Wikipedia articles and Israeli tech news. Our empirical investigation reveals that standard evaluation metrics such as F1 Scores and Exact Match (EM) are not appropriate for Hebrew (and other MRLs), and we propose a relevant enhancement. In addition, our experiments show low correlation between models' performance on morphosyntactic tasks and on MRC, which suggests that models that are designed for the former might underperform on semantics-heavy tasks. The development and exploration of HeQ illustrate some of the challenges MRLs pose in natural language understanding (NLU), fostering progression towards more and better NLU models for Hebrew and other MRLs.
Joint Hebrew segmentation and parsing using a PCFG-LA lattice parser
2011
We experiment with extending a lattice parsing methodology for parsing Hebrew to make use of a stronger syntactic model: the PCFG-LA Berkeley Parser. We show that the methodology is very effective: using a small training set of about 5500 trees, we construct a parser which parses and segments unsegmented Hebrew text with an F-score of almost 80%, an error reduction of over 20% over the best previous result for this task. This result indicates that lattice parsing with the Berkeley parser is an effective methodology for parsing over uncertain inputs.
Accurate Unlexicalized Parsing for Modern Hebrew
Lecture Notes in Computer Science, 2007
Many state-of-the-art statistical parsers for English can be viewed as Probabilistic Context-Free Grammars (PCFGs) acquired from treebanks consisting of phrase-structure trees enriched with a variety of contextual, derivational (e.g., markovization) and lexical information. In this paper we empirically investigate the applicability and adequacy of the unlexicalized variety of such parsing models to Modern Hebrew, a Semitic language that differs in structure and characteristics from English. We show that contrary to experience with parsing the WSJ, the markovized, head-driven unlexicalized variety does not necessarily outperform plain PCFGs for Semitic languages. We demonstrate that enriching unlexicalized PCFGs with morphologically marked agreement features percolated up the parse tree (e.g., definiteness) outperforms plain PCFGs as well as a simple head-driven variation on the MH treebank. We further show that an (unlexicalized) head-driven variety enriched with the same features achieves even better performance. We conclude that morphologically rich languages introduce an additional dimension of parametrization that is orthogonal to the horizontal/vertical dimensions proposed before [11] and its contribution is essential and complementary.
Noun phrase chunking in hebrew: Influence of lexical and morphological features
2006
We present a method for Noun Phrase chunking in Hebrew. We show that the traditional definition of base-NPs as nonrecursive noun phrases does not apply in Hebrew, and propose an alternative definition of Simple NPs. We review syntactic properties of Hebrew related to noun phrases, which indicate that the task of Hebrew SimpleNP chunking is harder than base-NP chunking in English. As a confirmation, we apply methods known to work well for English to Hebrew data. These methods give low results (F from 76 to 86) in Hebrew. We then discuss our method, which applies SVM induction over lexical and morphological features. Morphological features improve the average precision by ~0.5%, recall by ~1%, and F-measure by ~0.75, resulting in a system with average performance of 93% precision, 93.4% recall and 93.2 Fmeasure. *
This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs: fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances thereof: token-based and morpheme-based. Our experiments show that the effect of representational choices vary with architectural types. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavor also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89% accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks' task performance.
Tagging a Hebrew Corpus: the Case of Participles
2008
We report on an effort to build a corpus of Modern Hebrew tagged with parts of speech and morphology. We designed a tagset specific to Hebrew while focusing on four aspects: the tagset should be consistent with common linguistic knowledge; there should be maximal agreement among taggers as to the tags assigned to maintain consistency; the tagset should be useful for machine taggers and learning algorithms; and the tagset should be effective for applications relying on the tags as input features. In this paper, we illustrate these issues by explaining our decision to introduce a tag for beinoni forms in Hebrew. We explain how this tag is defined, and how it helped us improve manual tagging accuracy to a high-level, while improving automatic tagging and helping in the task of syntactic chunking.