Nazarin Computational Morphology ta Hanyar Neural Network: Cikakken Bincike

Teburin Abubuwan Ciki

1 Gabatarwa

Computational morphology tana wakiltar haɗuwar ilimin harshe (morphology) da hanyoyin lissafi, tana mai da hankali kan nazari da samar da sifofin kalmomi ta hanyar tsarin hanyoyin lissafi. Fannin ya sami ci gaba sosai daga tsarin da ya dogara da ƙa'idodi zuwa hanyoyin koyon injin da ke dogara da bayanai, tare da hanyoyin neural network yanzu sun mamaye fagen.

Ilimin morphology yana nazarin saɓanin tsari a cikin siffar kalma da ma'ana, yana ma'amala da morphemes - ƙananan raka'a masu ma'ana na harshe. Misali, kalmar "drivers" ta ƙunshi morphemes guda uku: "drive" (tushe), "-er" (ƙari na asali), da "-s" (ƙari na juzu'i). Computational morphology tana nufin sarrafa nazari da samar da irin waɗannan tsarin morphological ta atomatik.

2 Hanyoyin Neural Network a cikin Computational Morphology

2.1 Samfuran Encoder-Decoder

Tsarin encoder-decoder sun kawo juyin juya hali a cikin computational morphology tun lokacin da Kann da Schütze (2016a) suka gabatar da su a fagen. Waɗannan samfuran yawanci suna amfani da hanyoyin neural na yau da kullun (RNNs) ko masu canzawa don ɓoye jerin abubuwan shigarwa da kuma warware sifofin morphological da aka yi niyya.

2.2 Hanyoyin Hankali

Hanyoyin hankali suna ba da damar samfuran su mai da hankali ga sassan da suka dace na jerin shigarwa lokacin samar da sakamako, suna haɓaka aiki sosai akan ayyukan morphological kamar jujjuyawa da kamo asali.

2.3 Tsarin Transformer

Samfuran Transformer, musamman waɗanda suka dogara da tsarin da Vaswani et al. (2017) suka bayyana, sun nuna nasara mai ban mamaki a cikin ayyukan morphological saboda ikon su na ɗaukar dogon zangon dogaro da kuma ikon sarrafa layi daya.

3 Aiwatar da Fasaha

3.1 Tushen Lissafi

Babban tsarin lissafi na samfuran jeri-zuwa-jeri a cikin morphology yana biye da:

Idan aka ba da jerin shigarwa $X = (x_1, x_2, ..., x_n)$ da jerin manufa $Y = (y_1, y_2, ..., y_m)$, samfurin yana koyon haɓaka yuwuwar sharadi:

$P(Y|X) = \prod_{t=1}^m P(y_t|y_{<t}, X)$

Inda yawanci ake lissafta rarraba yuwuwar ta amfani da aikin softmax:

$P(y_t|y_{<t}, X) = \text{softmax}(W_o h_t + b_o)$

3.2 Tsarin Samfura

Samfuran morphological na zamani yawanci suna amfani da:

• Yadudduka na saka ciki don wakilcin harafi ko ƙaramin kalma
• Masu ɓoyewa na Bidirectional LSTM ko transformer
• Hanyoyin hankali don daidaitawa
• Binciken katako don warwarewa

3.3 Hanyar Horarwa

Ana horar da samfuran ta amfani da mafi girman kimantawar yiwuwa tare da asarar giciye-entropy:

$L(\theta) = -\sum_{(X,Y) \in D} \sum_{t=1}^m \log P(y_t|y_{<t}, X; \theta)$

4 Sakamakon Gwaji

Hanyoyin Neural sun nuna gagarumin ci gaba a fadin ma'auni da yawa:

Bayanin Ginshiƙi: Kwatancen ayyukan ya nuna samfuran neural suna samun ingantacciyar riba ta 15-25% akan hanyoyin gargajiya a cikin ayyuka da yawa da aka raba, tare da tsarin transformer akai-akai sun fi dacewa fiye da hanyoyin neural na farko.

5 Aiwatar da Lambar

A ƙasa akwai saukakken aiwatar da PyTorch na samfurin jujjuyawar morphological:

import torch
import torch.nn as nn
import torch.optim as optim

class MorphologicalInflectionModel(nn.Module):
    def __init__(self, vocab_size, embed_dim, hidden_dim, output_dim):
        super(MorphologicalInflectionModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.encoder = nn.LSTM(embed_dim, hidden_dim, batch_first=True, bidirectional=True)
        self.decoder = nn.LSTM(embed_dim, hidden_dim, batch_first=True)
        self.attention = nn.MultiheadAttention(hidden_dim, num_heads=8)
        self.output_layer = nn.Linear(hidden_dim, output_dim)
        self.dropout = nn.Dropout(0.3)
    
    def forward(self, source, target):
        # Encode source sequence
        source_embedded = self.embedding(source)
        encoder_output, (hidden, cell) = self.encoder(source_embedded)
        
        # Decode with attention
        target_embedded = self.embedding(target)
        decoder_output, _ = self.decoder(target_embedded, (hidden, cell))
        
        # Apply attention mechanism
        attn_output, _ = self.attention(decoder_output, encoder_output, encoder_output)
        
        # Generate output probabilities
        output = self.output_layer(self.dropout(attn_output))
        return output

# Training setup
model = MorphologicalInflectionModel(
    vocab_size=1000, 
    embed_dim=256, 
    hidden_dim=512, 
    output_dim=1000
)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss(ignore_index=0)

6 Aikace-aikace na Gaba da Alkibloli

Makomar computational morphology tare da hanyoyin neural ya haɗa da alkibloli masu ban sha'awa da yawa:

• Koyo na Ƙaramin Albarkatu: Haɓaka dabarai don nazarin morphological a cikin harsuna masu iyakataccen bayanan da aka yi wa lakabi
• Hanyoyin Modal daban-daban: Haɗa nazarin morphological tare da sauran matakan harshe
• Samfuran da za a iya fassara: Ƙirƙirar samfuran neural waɗanda ke ba da hasken ilimin harshe bayannan hasashe na baƙar fata
• Canja Harsuna: Yin amfani da ilimin morphological a cikin harsuna masu alaƙa
• Aikace-aikace na Ainihi-lokaci: Tura ingantattun samfura don na'urorin wayar hannu da na gefe

7 Nassoshi

1. Kann, K., & Schütze, H. (2016). Single-model encoder-decoder with explicit morphological representation for reinflection. Proceedings of the 2016 Meeting of SIGMORPHON.
2. Cotterell, R., Kirov, C., Sylak-Glassman, J., Walther, G., Vylomova, E., Xia, P., ... & Yarowsky, D. (2016). The SIGMORPHON 2016 shared task—morphological reinflection. Proceedings of the 2016 Meeting of SIGMORPHON.
3. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
4. Wu, S., Cotterell, R., & O'Donnell, T. (2021). Morphological irregularity correlates with frequency. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics.
5. Haspelmath, M., & Sims, A. D. (2013). Understanding morphology. Routledge.

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